100 tips on how to use me (from ai agent)
100 Tips on How to Use Me (From AI Agent)
Getting Started
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Start with clear, specific goals rather than vague requests.
Do: Specify the exact problem, component, file location, and expected behavior.
Don’t: Ask for general solutions like “Fix my app” without specifics.
Why it works: Specificity eliminates guesswork. When you precisely define your goal and provide file locations, the agent can immediately look at the relevant code instead of searching or asking follow-up questions.
Example:
“Fix the login form validation that fails when users enter special characters in the email field (code in src/components/Auth/LoginForm.jsx)”
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Break complex tasks into smaller, manageable chunks.
Do: Focus on one component, feature, or function at a time.
Don’t: Request large systems like “Build a full e-commerce platform” in one prompt.
Why it works: Smaller tasks have clearer requirements and fewer moving parts. The agent can focus deeply on one aspect, producing higher quality code with fewer assumptions.
Example:
“Let’s create a product card component that displays image, title, price, and ‘Add to Cart’ button. Here’s our product schema: { id: string, title: string, price: number, imageUrl: string, inventory: number }. Please check our existing components in src/components/ui/ to match our styling.”
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Provide context about your project before asking for implementation.
Do: Share your tech stack, existing architecture, and project constraints, plus relevant files.
Don’t: Ask for solutions like “How do I implement authentication?” without providing implementation context.
Why it works: Context allows the agent to provide tailored solutions that integrate with your specific environment. Seeing your actual code prevents mismatches with your architecture.
Example:
“I need to implement email authentication in my React app with Firebase. Please check my src/context/AuthContext.js and src/services/firebase.js files to understand our current setup before implementing the solution.”
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Specify programming language and framework upfront, including versions.
Do: Name the exact language, version, preferred libraries, and data format details.
Don’t: Make vague requests like “Parse this CSV file” without specifying technology.
Why it works: Different languages and framework versions have different capabilities. Specifying versions ensures compatible solutions that leverage appropriate features.
Example:
“Parse this CSV file using Python 3.10 with pandas 2.0. The file has semicolon separators and includes header rows. Here are the first 5 lines of my CSV file: [paste sample]. Could you analyze this sample to identify data types before implementing?”
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Share relevant file paths to avoid wasted time searching.
Do: Include the exact location of files you want to modify and what changes are needed.
Don’t: Make generic requests like “Update the user model” without specifying location.
Why it works: File paths immediately direct the agent to the right code, eliminating search time. The agent can also check related files for dependencies.
Example:
“Update the user model in src/models/User.ts to add a ‘preferences’ field that stores notification settings. Before updating, could you check src/models/User.ts and also look for files importing this model to understand what might be affected by this change?”
Project Understanding
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Link to the repository README if available or have the agent read it.
Do: Ask the agent to read documentation that provides high-level project overview.
Don’t: Assume the agent understands your project’s architecture without context.
Why it works: READMEs typically contain valuable information about project structure, setup, and conventions that help the agent understand your codebase’s organization.
Example:
“Please help me implement a new feature for user profiles. Start by reading our project’s README.md in the root directory to understand our project structure and conventions, then examine our folder structure to identify where components, hooks, and utilities are stored.”
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Summarize key project dependencies and architecture, or ask the agent to find them.
Do: List major libraries or ask the agent to find them in your package.json/requirements files.
Don’t: Expect the agent to infer your tech stack without checking dependency files.
Why it works: Understanding your dependencies helps the agent leverage built-in capabilities and avoid suggesting incompatible solutions.
Example:
“I need help optimizing our API calls. We’re using Next.js 14 with React Query, Tailwind, and Prisma with PostgreSQL. Could you first look at our package.json to confirm all dependencies and their versions, then check our API utilities in src/lib/api.js?”
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Explain your coding style preferences or ask the agent to detect them.
Do: Ask the agent to review existing files to detect your style patterns.
Don’t: Leave the agent to guess your preferred coding style.
Why it works: Style consistency is crucial for maintainability. By detecting patterns from existing code, the agent can match your codebase conventions.
Example:
“Create a new React component for displaying user notifications. Before implementing, please review a few files in our src/components directory to understand our coding style - we use functional components with hooks, prefer destructuring, and organize exports in index files.”
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Ask the agent to identify testing frameworks and linting tools you use.
Do: Have the agent detect your testing approach by examining existing tests and configuration.
Don’t: Assume the agent knows your testing strategy without examples.
Why it works: Consistency in testing is essential. By examining existing tests, the agent can match your conventions for test organization, mocking, and assertions.
Example:
“Write tests for our new authentication component. Before writing tests, please check our project to identify what testing framework and patterns we’re using. Look at our package.json test scripts, then check test files in our src/tests directory to understand our testing patterns.”
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Have the agent identify unusual project conventions by examining your code.
Do: Ask the agent to detect non-standard patterns in your codebase.
Don’t: Assume all projects follow standard conventions that the agent can guess.
Why it works: Unusual conventions are often consistent within a project but may surprise the agent. Having it discover your patterns ensures alignment with your approach.
Example:
“Help me implement a new feature following our project conventions. Our project has some unique conventions. Could you look at our component structure and identify how we organize component props, styling, and state management by examining several components in different directories?”
Writing Effective Prompts
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Start with the “what” before the “how,” then let the agent explore implementation options.
Do: Clearly state your objective, then let the agent determine the implementation details after examining your code.
Don’t: Prescribe implementation with requests like “Use Jest to write tests for the authentication flow” before the agent understands your codebase.
Why it works: Starting with the goal ensures the agent understands what you’re trying to accomplish. Letting it explore your code first often leads to better-integrated solutions.
Example:
“I need to test our user authentication flow, focusing on login, registration, and password reset. Please look at our auth components in src/auth and existing tests to determine the best approach. We haven’t settled on a testing strategy yet.”
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Use technical terminology precisely and ask the agent to analyze relevant code.
Do: Use specific technical terms and direct the agent to examine relevant code files.
Don’t: Use vague terms like “The app database isn’t working” without providing technical details.
Why it works: Precise terminology coupled with code examination eliminates ambiguity and provides the agent with both the problem statement and implementation context.
Example:
“Our PostgreSQL queries to the users table are timing out after 30 seconds. Please examine our src/db/user.js file containing these queries and our database schema in src/db/schema.sql to identify potential optimization issues.”
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Avoid ambiguous pronouns and references; instead, have the agent explore connections.
Do: Use specific nouns to reference components and direct the agent to trace the interactions between components.
Don’t: Use vague references like “It’s not working when I click it” without clear context.
Why it works: Clear references plus guided code exploration prevent misunderstandings about which part of the system you’re discussing.
Example:
“The submit button in src/components/Form.jsx doesn’t trigger form validation when clicked. Please examine this component and its parent component in src/pages/Contact.jsx to understand the event flow and why validation might not be triggering.”
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Include relevant error messages verbatim and ask the agent to search for similar patterns.
Do: Copy and paste exact error messages and ask the agent to find similar risky patterns.
Don’t: Paraphrase or summarize error messages.
Why it works: Exact error messages help the agent identify specific patterns to search for throughout your codebase, potentially preventing similar issues elsewhere.
Example:
“I’m getting
TypeError: Cannot read property 'id' of undefinedwhen clicking save. Here’s the stack trace: [paste]. Could you search our codebase for similar patterns where we might be accessing properties without checking existence first?”
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Specify output format and ask the agent to derive style from your existing code.
Do: Ask the agent to examine your existing code to match your style before generating new code.
Don’t: Leave style requirements implicit.
Why it works: Having the agent analyze your code first ensures new code matches your existing patterns for imports, error handling, comments, and typing.
Example:
“Generate a TypeScript utility function for date formatting. Before implementing, please check our existing utility functions in src/utils/ to match our code style, error handling patterns, and documentation approach.”
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Have the agent check for version-specific features in your codebase.
Do: Ask the agent to identify how you’re using version-specific features in your existing code.
Don’t: Assume the agent will automatically use the right version-specific patterns.
Why it works: Each version introduces new patterns and best practices. Having the agent check your usage ensures consistent approach to version-specific features.
Example:
“Implement a component using React 18’s new features. We’re using React 18 with TypeScript 4.9. Before implementing, please check our existing components to see how we’re using React 18 features like useTransition or Suspense.”
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For new features, describe expected inputs and outputs, then ask the agent to check similar features.
Do: Define parameter types and return values, then direct the agent to similar existing functions.
Don’t: Request new features without checking how similar functionality is implemented.
Why it works: Examining similar functions ensures the new code follows your established patterns for argument validation, error handling, and return types.
Example:
“Create a function that takes a user ID (string) and returns user permissions (string[]). Before implementing, please check how we handle similar user-related functions in src/services/user.js to understand our patterns for API calls, error handling, and data transformation.”
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For bugs, include steps to reproduce and ask the agent to trace through relevant code.
Do: Provide numbered steps and the files involved in each step of the flow.
Don’t: Describe intermittent behavior without pointing to relevant files.
Why it works: Following the code path with specific files helps the agent understand the execution flow and identify where assumptions might be breaking.
Example:
“Bug: When a user logs in, then navigates to settings, then clicks ‘Save profile’ without making changes, they get an error. Please trace through this flow by examining: 1) src/pages/Login.jsx, 2) src/pages/Settings.jsx, 3) src/services/profile.js to understand how user data flows through our app.”
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For refactors, explain the motivation and ask the agent to analyze impact.
Do: Clarify the refactoring goal and ask the agent to map out affected files.
Don’t: Request refactoring without asking the agent to assess the impact.
Why it works: Understanding dependencies helps ensure that refactoring doesn’t break existing functionality. The agent can create a more comprehensive plan by mapping affected areas.
Example:
“We need to refactor our authentication to support multiple providers (currently only email in src/auth/email.js). Before implementing, please analyze which files import our current auth module to understand the scope of changes needed.”
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Use formatting for your requests and ask the agent to analyze your code structure first.
Do: Structure your prompt with clear sections and direct the agent to analyze specific aspects of your codebase.
Don’t: Write wall-of-text prompts without guiding the agent through your code organization.
Why it works: Well-structured prompts with code exploration guidance ensure the agent understands your architecture before implementing solutions.
Example:
“Implement a new notification system. Before implementing, please analyze our project structure to understand:
- How we organize components (check src/components)
- Our state management approach (check src/store)
- Where we keep utilities (check src/utils) Then implement the notification system following these patterns.”
Collaboration Strategies
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Provide feedback on generated code to refine future results and ask for improvements.
Do: Explain what aspects could be improved and ask the agent to verify patterns across similar files.
Don’t: Accept solutions that don’t match your codebase’s patterns.
Why it works: Feedback helps align the agent with your preferences, while asking it to check other files ensures broader consistency.
Example:
“This solution works, but we prefer async/await over Promises with .then(). Could you refactor this and also check other files in src/services/ to ensure we’re using async/await consistently throughout our codebase?”
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Use “continue” when you need more output on the same topic and provide additional context.
Do: Acknowledge what’s been provided, specify what more you need, and point to examples.
Don’t: Ask to continue without directing the agent to relevant examples in your codebase.
Why it works: “Continue” maintains conversation context while pointing to examples ensures the new work matches your patterns.
Example:
“That’s a good start with the error handling. Please continue by adding unit tests. You can check our existing test patterns in src/tests/services/ to match our testing style. Before continuing with the tests, please examine a few existing test files to understand our patterns for mocking, assertions, and test organization.”
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When confused by generated code, ask the agent to explain and provide examples from your codebase.
Do: Ask for explanations and request that the agent find similar patterns in your code.
Don’t: Proceed with implementing code you don’t understand.
Why it works: Seeing explanations alongside similar patterns from your codebase helps connect unfamiliar concepts to your existing knowledge.
Example:
“I’m confused by this reducer implementation. Could you explain how it works? Also, please show me similar patterns from our existing reducers in src/store/reducers/ that follow the same approach so I can understand it in context.”
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Request alternative approaches that fit your codebase patterns.
Do: Ask for multiple implementations after the agent examines your coding patterns.
Don’t: Settle for the first solution without checking if it matches your codebase style.
Why it works: Having the agent check your existing patterns first ensures alternatives are presented in a style consistent with your codebase.
Example:
“Can you show me both a recursive and iterative approach to this problem? Before implementing, check our existing utility functions to see which style is more consistent with our codebase so I can choose the approach that best fits our patterns.”
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Ask for complexity analysis in the context of your actual data volume.
Do: Request complexity analysis and ask the agent to examine how you currently handle scale.
Don’t: Assume all solutions will perform adequately with your data volume.
Why it works: Connecting complexity analysis to your existing patterns for handling large datasets ensures solutions that align with your performance needs.
Example:
“What’s the time and space complexity of this solution? We’ll be processing around 1M records. Could you check our src/services/data.js file to see how we currently handle large datasets to ensure this solution is consistent with our performance patterns?”
Code Generation
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Request comments that match your codebase’s documentation style.
Do: Ask the agent to examine your existing code comments to match style.
Don’t: Accept comments that don’t match your documentation patterns.
Why it works: Consistent comment styles improve readability. Having the agent analyze your existing comments ensures stylistic consistency.
Example:
“Please add detailed comments to this graph traversal algorithm. First, check a few complex functions in our src/utils/ directory to match our commenting style and detail level, then apply the same approach to this code.”
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Have the agent analyze your error handling patterns before implementing new code.
Do: Ask the agent to detect your error handling patterns from existing code.
Don’t: Assume there’s a universal “right way” to handle errors.
Why it works: Error handling approaches vary widely between codebases. Having the agent analyze your patterns ensures consistency.
Example:
“Implement error handling for this API call. First, analyze our src/services/ files to understand how we handle errors consistently across our application. Check several API call implementations to identify our error handling patterns.”
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Share performance constraints and ask the agent to analyze similar high-performance code in your codebase.
Do: Specify performance requirements and direct the agent to similar optimized code.
Don’t: Request optimization without pointing to examples of how you currently handle performance-critical code.
Why it works: Your codebase likely has established patterns for optimization. Having the agent analyze these ensures consistent performance practices.
Example:
“This function will be called 100 times per second, so it needs to be optimized. Before implementing, please examine our other high-frequency functions in src/services/realtime/ to see how we optimize similar cases, looking for techniques like memoization, buffer pooling, or throttling.”
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Request idiomatic code by asking the agent to analyze your established patterns.
Do: Ask the agent to detect your specific idioms and conventions before writing new code.
Don’t: Assume the agent knows your specific interpretations of language idioms.
Why it works: Even within language communities, teams develop specific conventions. Having the agent analyze your code ensures it matches your team’s interpretation of idiomatic code.
Example:
“Write this following our Go coding conventions. First, analyze our existing Go files in cmd/ and internal/ directories to identify our patterns for error handling, logging, and function signatures before implementing.”
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Ask for examples that match your codebase’s testing patterns.
Do: Request examples that also demonstrate your testing approach.
Don’t: Accept examples that wouldn’t translate well to your test suite.
Why it works: Examples that match your testing style serve dual purposes - they demonstrate usage and can be adapted into your test suite with minimal changes.
Example:
“After implementing the function, show a few example calls with different inputs. Also, write tests following the patterns in our src/tests directory. Before creating examples, check how we write tests in our codebase so your examples can easily be adapted into our test suite.”
Debugging Assistance
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Share the full error stack trace and ask the agent to search your codebase for similar patterns.
Do: Provide the complete error information and ask the agent to examine the specific files in the stack trace plus search for similar patterns.
Don’t: Share partial error information without asking for code exploration.
Why it works: Having the agent trace through the actual code path in the stack trace while also looking for similar patterns helps identify both the specific issue and potential systemic problems.
Example:
“Here’s my complete error stack: [paste multi-line stack trace]. Please search our codebase for similar error patterns and examine the files mentioned in the stack trace to identify the root cause.”
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Describe what you’ve already tried and ask the agent to explore new directions.
Do: List what you’ve ruled out and direct the agent to explore specific alternative areas.
Don’t: Just say what you’ve tried without guiding further exploration.
Why it works: Directing the agent to specific unexplored areas after ruling out common causes helps it focus on likely remaining issues.
Example:
“I’ve already checked that the database connection is working and the table exists. Could you examine our query builder in src/db/builder.js and our model in src/models/User.js to identify other potential causes for the timeouts?”
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For minimal reproduction cases, ask the agent to identify the core issue in your actual code.
Do: Share the minimal case, then ask the agent to find where this pattern appears in your actual code.
Don’t: Stop at the minimal reproduction without connecting it back to your codebase.
Why it works: Minimal cases clarify the issue, but finding the pattern in your actual code reveals where and how to fix it in context.
Example:
“I’ve narrowed the bug down to this minimal case: [simple code]. Now please examine our actual implementation in src/components/DataTable.jsx to see where this issue might be occurring in our complex component.”
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Share your mental model and ask the agent to validate it against your code.
Do: Explain your hypothesis and ask the agent to check specific code areas that would confirm or refute it.
Don’t: Present theories without directing the agent to investigate them in your code.
Why it works: Having the agent check your hypothesis against actual code either validates your understanding or reveals misconceptions.
Example:
“I think the issue is related to the component remounting, but I’m not sure why. Could you examine our src/components/Profile.jsx and its parent components to see if there’s unnecessary remounting happening? Please look at how we’re handling state and props.”
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Have the agent analyze your configuration in context of your application code.
Do: Share configuration values and ask the agent to trace how they’re used in your code.
Don’t: Discuss configuration without examining implementation.
Why it works: Configuration values only matter in the context of how they’re used. Having the agent trace their usage reveals how they actually impact behavior.
Example:
“I have NODE_ENV=development and DEBUG=true, but logging isn’t working. Please examine our src/config/logger.js and anywhere it’s imported to see how environment variables affect logging behavior and why my settings might not be having the expected effect.”
Optimization
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Provide current and target performance metrics and ask the agent to profile your code.
Do: Specify measurable metrics and ask the agent to analyze specific performance factors in your code.
Don’t: Request generic optimization without directing analysis to likely bottlenecks.
Why it works: Having the agent examine specific optimization factors (like database indexes or algorithm choices) focuses the effort on high-impact improvements.
Example:
“This query in src/db/reports.js currently takes 2s to run. We need it under 500ms. Please analyze the query, our schema in src/db/schema.sql, and check for missing indexes or inefficient joins. First analyze our database schema, then check the query structure for inefficient patterns like N+1 queries.”
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Share profiling data and ask the agent to find similar patterns throughout your codebase.
Do: Share profiling results and ask the agent to find similar inefficient patterns elsewhere.
Don’t: Focus only on the identified hot spot without looking for similar issues.
Why it works: Performance issues often follow patterns. Finding and fixing all instances of an inefficient pattern multiplies the impact of your optimization work.
Example:
“Chrome DevTools shows the bottleneck is in this sorting function. Please examine src/utils/sort.js and find other places in our codebase that might have similar inefficient sorting patterns so we can fix them all at once.”
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Specify which dimensions to optimize and ask the agent to analyze tradeoffs in your specific codebase.
Do: Clarify optimization priorities and ask the agent to analyze current approaches in your code.
Don’t: Assume all optimizations are equally valuable without examining your specific constraints.
Why it works: Optimization always involves tradeoffs. Having the agent understand your current approach helps it suggest improvements that preserve necessary characteristics while enhancing priority dimensions.
Example:
“We need to optimize this function for memory usage over execution speed. Please examine our src/services/imageProcessing.js file to understand our current approach and suggest memory-efficient alternatives that maintain acceptable speed.”
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Describe hardware constraints and ask the agent to find similar optimizations in your codebase.
Do: Specify environment limitations and direct the agent to look for similar optimizations in your code.
Don’t: Mention constraints without checking your existing approaches to similar limitations.
Why it works: You likely have established patterns for handling specific constraints. Finding these helps ensure consistent optimization strategies.
Example:
“This code needs to run on low-end mobile devices with limited RAM. Please examine our src/utils/optimizations.js file to see how we’ve handled similar constraints elsewhere in our codebase.”
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Share your benchmarking approach and ask the agent to improve both code and benchmarks.
Do: Share benchmark code and ask the agent to evaluate both the code and the measurement approach.
Don’t: Focus solely on the function being optimized without examining how you’re measuring performance.
Why it works: Sometimes benchmark methodology introduces bias or misses important factors. Having the agent examine both the code and how you’re measuring it ensures comprehensive optimization.
Example:
“Here’s how we’re benchmarking this function in src/benchmarks/api.js. Please analyze both the benchmark methodology and the function being tested in src/api/users.js to identify improvements. First check if our benchmark methodology accurately reflects real-world usage patterns.”
Testing
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Ask the agent to analyze your edge cases by examining validation and error handling.
Do: Direct the agent to analyze your validation and error handling code to identify edge cases.
Don’t: Expect the agent to guess what edge cases matter without examining your code.
Why it works: Your validation and error handling often reveal what edge cases you care about. Having the agent analyze these helps identify the most relevant scenarios to test.
Example:
“Please write tests for our user registration function, focusing on edge cases. First, examine src/validators/user.js and src/services/auth.js to identify input validation and error handling that reveals edge cases we already handle, then build tests around those plus any others you identify.”
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Request property-based testing ideas based on your data models and constraints.
Do: Ask the agent to analyze your data models and constraints before suggesting properties to test.
Don’t: Request property-based tests without asking the agent to understand your specific data structures.
Why it works: Effective property-based testing requires understanding what properties should hold true for your specific data structures and operations.
Example:
“Suggest properties we could verify with property-based testing for our sorting algorithm. First, check our src/models/SortableCollection.js to understand our data structures and invariants that should be maintained by our sorting function.”
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Have the agent analyze your test patterns across different types of tests.
Do: Ask the agent to analyze your testing patterns across different test types before writing new tests.
Don’t: Assume there’s a universal “right way” to write tests that the agent should follow.
Why it works: Testing approaches vary widely between teams. Having the agent analyze your specific patterns ensures new tests integrate seamlessly with your suite.
Example:
“Write tests for this new component. Before writing tests, please analyze our existing tests in src/tests/ to understand how we structure unit tests vs integration tests, how we mock dependencies, and how we test async code.”
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Request integration test scenarios based on your actual component interactions.
Do: Ask the agent to analyze the actual integration points in your code to determine test scenarios.
Don’t: Request generic integration tests without examining your specific implementation.
Why it works: Effective integration tests focus on the actual interaction points and failure modes in your specific implementation.
Example:
“What integration tests should we add to verify this payment processor integration? Please examine src/services/payment.js and src/hooks/useCheckout.js to understand the interaction points and analyze how our payment service is called and how results are handled.”
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Ask the agent to identify your mocking patterns before writing new tests.
Do: Direct the agent to analyze your existing mocks to maintain consistency.
Don’t: Implement new mocking approaches without checking your established patterns.
Why it works: Consistent mocking strategies make tests easier to understand and maintain. Having the agent check your patterns ensures this consistency.
Example:
“How should we mock the AWS S3 client in these tests? Please examine our existing tests in src/tests/services/ to see how we currently mock external services and identify our preferred patterns and tools.”
Documentation
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Ask the agent to analyze documentation for different audience types in your codebase.
Do: Have the agent examine existing documentation targeting different audiences before writing new docs.
Don’t: Assume the agent knows how to structure documentation for different audiences without examples.
Why it works: Documentation style and detail level vary greatly based on audience. Analyzing your existing approach ensures consistency.
Example:
“Write documentation for our new API endpoint for both API consumers and internal developers. First, examine our existing API docs in docs/api/ and internal docs in src/README.md to understand how we target different audiences with different styles, detail levels, and formats.”
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Request examples based on actual usage patterns in your codebase.
Do: Ask the agent to analyze actual usage patterns in your code before creating examples.
Don’t: Accept generic examples that don’t match how features are actually used in your application.
Why it works: The most helpful examples reflect actual usage patterns. Having the agent analyze your code ensures examples demonstrate realistic scenarios.
Example:
“Include examples showing pagination and filtering for this API endpoint. First, please examine src/services/api.js and src/hooks/useData.js to see how these features are typically used in our application before writing examples.”
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Have the agent detect your documentation format from existing files.
Do: Direct the agent to analyze similar existing documentation before creating new docs.
Don’t: Specify a documentation format without checking if it matches your existing approach.
Why it works: Consistent documentation formats improve discoverability and readability. Having the agent check your existing docs ensures this consistency.
Example:
“Generate documentation for this class. First, check our other class documentation in src/models/ to match our format, style, and level of detail.”
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Ask for diagram descriptions based on analyzing your architecture.
Do: Have the agent analyze actual code flows before creating architectural diagrams.
Don’t: Request diagrams based on assumptions about how your system works.
Why it works: Accurate diagrams require understanding the actual implementation. Having the agent trace through the code ensures diagrams reflect reality rather than idealized design.
Example:
“Describe a sequence diagram showing how our authentication flow works. First, analyze the code flow through src/services/auth.js, src/hooks/useAuth.js, and src/context/AuthContext.js, tracing through the code execution from login initiation through completion.”
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Request rationales that incorporate your existing architectural decisions.
Do: Ask the agent to find existing rationales in your codebase before explaining decisions.
Don’t: Accept generic explanations that don’t account for your specific context.
Why it works: Architectural decisions often build on previous choices. Having the agent check for existing rationales ensures explanations account for your full context.
Example:
“Explain why we’re using this pattern for state management. First, analyze our src/store/ implementation and comments in src/README.md that might explain our architectural decisions before providing explanations.”
Learning
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Specify your knowledge level by referencing familiar concepts in your codebase.
Do: Reference specific parts of your codebase you understand and parts you don’t.
Don’t: Make vague statements about your knowledge level without connecting to specific code.
Why it works: Referencing specific code you understand gives the agent a precise gauge of your knowledge and a starting point for building explanations.
Example:
“I understand how we use React hooks in src/hooks/useData.js, but I’m confused about the context implementation in src/context/. Can you explain our context approach assuming I understand the hooks pattern? Please compare and contrast our context implementation with our hooks implementation.”
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Ask for analogies that connect new concepts to familiar patterns in your codebase.
Do: Reference specific implementations in your code that you already understand.
Don’t: Ask for analogies to concepts you might not fully understand.
Why it works: Concrete references to your own code provide the agent with a precise understanding of your knowledge foundation.
Example:
“Explain React’s useEffect cleanup function. Explain it like I understand event listeners in our src/utils/events.js implementation. Compare the cleanup pattern in useEffect to how we manage subscription cleanup in our events utility.”
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Ask the agent to identify learning resources mentioned in your codebase.
Do: Have the agent check if your team has already documented learning resources.
Don’t: Jump straight to external resources without checking internal documentation.
Why it works: Teams often document recommended learning resources that align with their specific approach. These are usually more relevant than generic recommendations.
Example:
“What are good resources to learn more about database indexing strategies? Check our src/db/README.md and comments in our schema files first to see if there are already recommended resources our team has found valuable.”
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Ask the agent to trace the evolution of a pattern through your codebase history.
Do: Direct the agent to compare older and newer implementations in your code.
Don’t: Ask about evolution without pointing to artifacts showing different stages.
Why it works: Comparing different generations of code in your project reveals the practical evolution of patterns in your specific context.
Example:
“How has our state management evolved in this project? Look at older components in src/legacy/ compared to newer ones in src/components/ to identify changes in approach through different generations of our code.”
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Request comparisons based on your specific implementation constraints.
Do: Ask the agent to analyze your current implementation and constraints before comparing alternatives.
Don’t: Request generic comparisons without considering your specific context.
Why it works: The best approach depends heavily on your specific constraints and requirements. Having the agent analyze these ensures relevant comparisons.
Example:
“Compare JWT vs. session-based authentication for our use case. First, examine our current auth implementation in src/services/auth.js and our server constraints in server/config.js to ensure recommendations account for our specific needs.”
Architecture
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Share your design ideas with code links and ask for analysis based on your codebase.
Do: Provide links to relevant code and ask the agent to consider your current implementation.
Don’t: Discuss architectural changes in the abstract without examining your starting point.
Why it works: Architectural changes must account for your current state. Having the agent analyze your existing implementation ensures practical, contextual advice.
Example:
“Here’s my diagram for a microservice architecture. What would you improve? Before giving feedback, please examine our current monolith in src/ to understand what would need to change and what challenges we might face.”
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Ask the agent to identify existing patterns in your codebase before suggesting new ones.
Do: Have the agent analyze your existing patterns before suggesting new ones.
Don’t: Introduce new patterns without considering your existing architectural approach.
Why it works: Consistent patterns make codebases more maintainable. Having the agent understand your current patterns helps it suggest compatible additions.
Example:
“What design pattern would fit this workflow where users can undo multiple actions? First, analyze src/features/editor/ to see what patterns we’re already using for user actions and state management so we can extend existing approaches.”
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Describe scaling requirements and ask the agent to analyze current bottlenecks.
Do: Specify scale factors and direct the agent to examine specific components for bottlenecks.
Don’t: Discuss scaling in the abstract without examining your current implementation.
Why it works: Effective scaling requires understanding specific bottlenecks in your actual implementation. Having the agent analyze your code focuses on practical improvements.
Example:
“We need to modify this service to handle 10x the current traffic. Please analyze src/services/api.js and src/db/queries.js to identify potential bottlenecks like N+1 queries, missing caching, or operations that could be parallelized.”
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Have the agent analyze your codebase for architectural consistency before suggesting changes.
Do: Ask the agent to analyze your current implementation to understand your specific context.
Don’t: Accept generic tradeoff analysis without considering your specific use case.
Why it works: Architectural decisions must account for your specific constraints and requirements. Having the agent analyze your code ensures contextual recommendations.
Example:
“What are the tradeoffs between GraphQL and REST for our mobile app’s API? First, examine our current API implementation in src/api/ and mobile client in mobile/src/api/ to understand our specific requirements and data access patterns.”
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Ask the agent to trace actual code execution paths for complex interactions.
Do: Direct the agent to trace through actual code execution rather than making assumptions.
Don’t: Accept sequence diagrams based on assumptions about how your code works.
Why it works: Accurate sequence diagrams require understanding the actual implementation. Tracing through code reveals the true flow, which may differ from what’s assumed.
Example:
“Please describe a sequence diagram for our checkout process. Trace through the code flow starting from src/pages/Checkout.jsx through our services, API calls, and state updates to ensure an accurate diagram.”
Code Review
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Ask the agent to review specific aspects by checking against patterns in your codebase.
Do: Direct the agent to compare against your established patterns when reviewing specific aspects.
Don’t: Request generic reviews without providing context about your patterns.
Why it works: Effective reviews compare code against established patterns. Having the agent check your security patterns ensures consistent standards.
Example:
“Review this authentication code for security vulnerabilities. First, check our security patterns in src/utils/security.js and other authentication implementations to identify any deviations from our established security practices.”
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Have the agent detect your style conventions from multiple files before checking consistency.
Do: Ask the agent to derive your conventions from multiple existing files.
Don’t: Assume the agent knows your style conventions without examples.
Why it works: Style conventions vary between teams. Having the agent analyze multiple files helps it identify consistent patterns rather than anomalies.
Example:
“Does this new code follow our style conventions? Please analyze several files in src/components/ to identify our patterns for formatting, naming, and organization, then check if this code matches our established patterns.”
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Ask the agent to identify edge cases by analyzing validation and error handling in similar components.
Do: Direct the agent to find similar functionality and analyze how edge cases are handled.
Don’t: Expect the agent to identify relevant edge cases without context.
Why it works: Similar components often handle similar edge cases. Analyzing patterns across components helps identify comprehensive edge case coverage.
Example:
“What edge cases should I handle in this file upload function? Please look at src/components/forms/ImageUpload.jsx and other file handling components to see how we handle issues like large files, invalid formats, network interruptions, and server errors.”
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Have the agent analyze your code organization to identify architectural smells.
Do: Ask the agent to compare against your established patterns to identify anomalies.
Don’t: Request code smell identification without providing context about your architecture.
Why it works: “Code smells” are often relative to established patterns. Having the agent understand your typical approach helps identify meaningful deviations.
Example:
“What code smells do you notice in this controller? It feels too complex. Please analyze other controllers in src/controllers/ to understand our typical patterns and identify deviations in size, responsibility scope, and abstraction level.”
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Ask the agent to find examples of similar complex logic in your codebase that’s handled cleanly.
Do: Direct the agent to find similar patterns in your code that are implemented clearly.
Don’t: Accept generic readability improvements without checking your established patterns.
Why it works: Your codebase likely has established patterns for handling complex logic. Finding these provides contextually appropriate solutions.
Example:
“How can I make this complex boolean logic more readable? Please find other examples in our codebase where we handle complex conditions cleanly, particularly in src/utils/ or src/services/, to identify our established patterns for readable conditionals.”
Refactoring
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Specify refactoring goals and ask the agent to analyze dependencies in your codebase.
Do: Have the agent map dependencies and coupling before suggesting refactoring approaches.
Don’t: Request refactoring without analyzing the impact across your codebase.
Why it works: Effective refactoring requires understanding all affected components. Having the agent map dependencies ensures comprehensive changes.
Example:
“Refactor this class to reduce coupling with the payment service. First, analyze src/services/payment.js and all files that import it to understand the current coupling points and the full scope of impact.”
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Ask the agent to identify natural boundaries in your code for incremental refactoring.
Do: Have the agent analyze your code structure to identify natural incremental boundaries.
Don’t: Attempt large-scale refactoring without planning incremental steps.
Why it works: Code often has natural boundaries where changes can be safely isolated. Having the agent identify these enables safer, incremental refactoring.
Example:
“Break down this refactoring into smaller, deployable steps. Analyze the component structure and data flow to identify natural boundaries where changes can be safely isolated, focusing on component dependencies and state management.”
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Have the agent analyze consumer code to understand backward compatibility requirements.
Do: Ask the agent to analyze all consumers of the code being refactored.
Don’t: Focus solely on the implementation being refactored without considering consumers.
Why it works: Understanding all usage patterns is essential for maintaining compatibility. Having the agent analyze consumer code reveals what patterns must be preserved.
Example:
“How can we refactor this API while maintaining backward compatibility? Please examine all files in src/features/ that call this API to understand how it’s currently used and what parameters, return values, and behaviors clients depend on.”
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Ask the agent to trace execution paths to identify what can safely change.
Do: Have the agent analyze execution paths to distinguish public interfaces from internal details.
Don’t: Assume the boundary between public and private is always clear without analysis.
Why it works: Tracing execution paths reveals which aspects are truly internal implementation details versus which are effectively part of the public contract.
Example:
“Refactor this while keeping the public interface the same. Trace the execution path from public methods through private helpers to identify what can change internally without affecting callers. Analyze the call graph to determine which methods are only used internally.”
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Ask the agent to analyze similar refactorings in your commit history.
Do: Have the agent look for similar completed refactorings in your codebase.
Don’t: Approach each refactoring as a unique problem without learning from previous work.
Why it works: Teams often develop consistent approaches to common refactorings. Finding similar examples ensures consistency and leverages proven approaches.
Example:
“Refactor this to use async/await with minimal changes to surrounding code. Can you check if we’ve done similar refactoring elsewhere in our codebase that we could use as a pattern? Look for other places where we’ve refactored from callbacks or Promises.”
Security
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Ask the agent to compare your authentication implementation against security best practices.
Do: Have the agent analyze your specific implementation against current standards.
Don’t: Request generic security reviews without examining your actual implementation.
Why it works: Security reviews must account for your specific implementation details. Having the agent compare against best practices while examining your actual code ensures relevant findings.
Example:
“Review this JWT implementation for security issues. Compare our src/services/auth.js implementation against current best practices, checking token handling, expiration, and secret management.”
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Ask the agent to identify all input entry points in your application before discussing validation.
Do: Have the agent map input entry points and current validation approaches before making recommendations.
Don’t: Discuss validation in the abstract without understanding your current patterns.
Why it works: Consistent validation is essential for security. Having the agent analyze your current approaches ensures recommendations that fit your architecture.
Example:
“What’s the best way to validate and sanitize form input? First, analyze src/components/forms/ to identify all our input components and current validation approaches. Examine our form components, API endpoints, and URL parameters.”
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Have the agent search your codebase for common vulnerability patterns.
Do: Direct the agent to search for specific vulnerability patterns across relevant code areas.
Don’t: Focus security reviews on new code only without checking existing patterns.
Why it works: Vulnerability patterns often appear across a codebase. Having the agent conduct targeted searches helps identify systemic issues rather than isolated instances.
Example:
“Check our codebase for SQL injection vulnerabilities. Search for all database query construction in src/db/ and src/services/ to identify potentially unsafe string concatenation and verify we’re properly using parameterized queries.”
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Ask the agent to analyze your current patterns for handling sensitive data.
Do: Have the agent review your existing patterns for handling sensitive data before making recommendations.
Don’t: Implement new security patterns without understanding your current approach.
Why it works: Security improvements often need to work within existing architectural constraints. Understanding your current approach ensures practical recommendations.
Example:
“What’s the best pattern for storing user credentials in our database? First, analyze src/models/User.js and src/services/auth.js to understand our current approach to storing and managing sensitive user data, including hashing and encryption.”
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Ask the agent to identify all configuration sources before discussing secure configuration.
Do: Have the agent map all configuration sources and current security approaches.
Don’t: Discuss secure configuration without understanding your deployment environment.
Why it works: Secure configuration depends on your specific infrastructure and deployment practices. Having the agent analyze your current approach ensures contextual recommendations.
Example:
“How should we manage API keys securely in our environment? First, analyze src/config/, deployment files, and environment handling to understand how we currently load and manage configuration across different environments.”
DevOps
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Ask the agent to analyze your current CI/CD workflows before creating new ones.
Do: Have the agent analyze your build, test, and deployment processes before creating workflows.
Don’t: Create CI/CD pipelines without understanding your specific build requirements.
Why it works: Effective CI/CD must align with your specific build and test processes. Having the agent analyze these ensures appropriate automation.
Example:
“Create a GitHub Actions workflow for testing and deploying our Node.js app. First, examine our package.json scripts and any existing CI configurations to understand our build and test process.”
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Ask the agent to analyze existing Docker configurations in your project.
Do: Have the agent review your specific Docker configuration before suggesting improvements.
Don’t: Apply generic Docker optimizations without considering your specific application.
Why it works: Effective Docker optimization depends on your specific application type and dependencies. Having the agent analyze your configuration ensures targeted improvements.
Example:
“How can we reduce the size of this Docker image? Analyze our current Dockerfile and .dockerignore to identify optimization opportunities specific to our application dependencies and build process.”
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Have the agent create deployment checklists based on your specific application structure.
Do: Ask the agent to analyze your specific application components that require pre-deployment verification.
Don’t: Use generic deployment checklists without considering your application’s unique requirements.
Why it works: Effective deployment checklists must account for your specific architecture and dependencies. Having the agent analyze these creates a contextual checklist.
Example:
“What should we check before deploying this service to production? Analyze our src/config/, database migrations, and external dependencies to identify critical verification points specific to our application.”
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Ask the agent to identify key metrics based on your application’s critical paths.
Do: Have the agent analyze your application’s critical paths to determine important metrics.
Don’t: Monitor generic metrics without considering your specific failure modes.
Why it works: Effective monitoring focuses on metrics that indicate issues in your specific application. Having the agent analyze critical paths ensures relevant monitoring.
Example:
“What metrics should we monitor for this microservice? Analyze src/services/ and src/api/ to identify critical operations, external dependencies, and potential failure points in our core functionality.”
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Have the agent analyze database migration patterns in your codebase.
Do: Ask the agent to analyze your migration history and database usage before planning rollbacks.
Don’t: Create rollback plans without understanding your specific database interactions.
Why it works: Safe rollback strategies depend on your specific database usage patterns. Having the agent analyze these ensures practical recovery plans.
Example:
“What’s the safest way to roll back if this database migration fails? Analyze our previous migrations in migrations/ and database access patterns in src/db/ to determine potential issues with our foreign keys, indices, and data transformations.”
Working with APIs
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Ask the agent to analyze API documentation alongside your integration code.
Do: Have the agent compare your implementation against official documentation.
Don’t: Rely solely on either documentation or existing code without cross-checking.
Why it works: Comparing your implementation against official documentation helps identify misunderstandings or outdated patterns.
Example:
“I’m integrating with the Stripe API. Please analyze our current payment processing in src/services/payments.js alongside the Stripe API docs to ensure we’re following best practices for error handling and security.”
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Have the agent analyze your existing retry patterns before implementing new ones.
Do: Ask the agent to identify your existing retry patterns before implementing new ones.
Don’t: Implement inconsistent retry strategies across your application.
Why it works: Consistent retry strategies improve maintainability. Having the agent check your existing patterns ensures this consistency.
Example:
“Implement an exponential backoff strategy for retrying these API calls. First, check src/utils/api.js to see how we handle retries in other parts of our application to ensure consistency with our current retry mechanisms and error recovery patterns.”
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Ask the agent to identify all API consumption points before implementing rate limiting.
Do: Have the agent map all API call sites before implementing rate limiting.
Don’t: Implement rate limiting at a single point if the API is called from multiple places.
Why it works: Effective rate limiting requires understanding all consumption patterns. Having the agent map these ensures comprehensive implementation.
Example:
“How should we implement rate limiting to stay under 100 requests per minute? First, find all places in our codebase where we call this API to ensure comprehensive coverage and understand their frequency and importance.”
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Ask the agent to analyze your API usage patterns before designing clients.
Do: Have the agent analyze usage patterns before designing API clients.
Don’t: Design clients based on the API specification alone without considering usage patterns.
Why it works: Effective API clients should optimize for actual usage patterns. Having the agent analyze these ensures the client addresses real needs.
Example:
“Design a client for this API that handles authentication and request formatting. First, analyze how we use this API across our codebase to understand common patterns and requirements, identifying common operations and authentication needs.”
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Have the agent examine your testing infrastructure before suggesting API testing approaches.
Do: Ask the agent to understand your testing infrastructure before suggesting API testing approaches.
Don’t: Implement new testing patterns without considering your existing infrastructure.
Why it works: Effective API testing should leverage your existing testing infrastructure. Having the agent analyze this ensures compatible recommendations.
Example:
“How can we test this API integration without hitting the actual service? Analyze our src/tests/ directory and test utilities to understand our testing infrastructure, mocking patterns, and existing API tests.”
Data Processing
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Ask the agent to analyze sample data along with your processing requirements.
Do: Provide data samples and ask the agent to analyze your current processing patterns.
Don’t: Discuss data processing in the abstract without examining your specific requirements and patterns.
Why it works: Effective data processing depends on both the data characteristics and your processing constraints. Having the agent analyze both ensures appropriate recommendations.
Example:
“We need to process ~500MB CSV files with 20 columns and 1M rows. Here’s a sample of the data: [paste sample]. Please analyze our src/services/import.js to understand our current processing approach, resource constraints, and error handling.”
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Have the agent analyze your schema validation patterns across the codebase.
Do: Ask the agent to identify your existing validation patterns before suggesting new ones.
Don’t: Implement inconsistent validation approaches across your application.
Why it works: Consistent validation improves maintainability. Having the agent analyze your existing patterns ensures this consistency.
Example:
“What’s the best way to validate this nested JSON against our schema? First, examine src/validators/ and src/models/ to understand our current validation approaches and find examples of complex data validation in our codebase.”
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Ask the agent to analyze error handling in your data processing pipelines.
Do: Have the agent examine your current error handling before suggesting approaches for specific cases.
Don’t: Implement inconsistent error handling across your data pipeline.
Why it works: Consistent error handling improves reliability and debuggability. Having the agent analyze your patterns ensures this consistency.
Example:
“How should we handle rows with missing fields in this import process? Analyze src/services/import.js and src/utils/errors.js to understand our error handling patterns and how we currently deal with data anomalies.”
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Have the agent analyze resource usage patterns in your existing processing code.
Do: Ask the agent to examine your current resource usage patterns before suggesting batch processing approaches.
Don’t: Implement batch processing without understanding your environment constraints.
Why it works: Optimal batch sizes depend on your specific processing and resource characteristics. Having the agent analyze these ensures appropriate recommendations.
Example:
“What’s a good pattern for processing these records in batches of 1000? Analyze our src/services/processing.js to understand our current approaches to memory usage, concurrency, and how we balance throughput against resource consumption.”
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Ask the agent to analyze your data lifecycle requirements across systems.
Do: Have the agent examine your current data lifecycle management before suggesting improvements.
Don’t: Implement storage solutions without understanding your broader data management approach.
Why it works: Effective data lifecycle management must integrate with your overall data architecture. Having the agent analyze this ensures cohesive recommendations.
Example:
“We need to store processing results for 30 days for audit purposes. Analyze our src/services/audit.js and database schema to understand our current data retention implementation and approach to data aging, archiving, and purging.”
UI Interactions
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Ask the agent to analyze user workflows across components in your application.
Do: Have the agent examine similar existing workflows before implementing new ones.
Don’t: Design new interaction patterns without considering your existing UI conventions.
Why it works: Consistent interaction patterns improve usability. Having the agent analyze your existing patterns ensures this consistency.
Example:
“Users need to select multiple items, then apply bulk actions from a dropdown. Analyze our existing list components in src/components/lists/ to understand how we handle selection and actions, finding examples of multi-selection patterns in our existing components.”
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Have the agent analyze your accessibility patterns across components.
Do: Ask the agent to identify your existing accessibility patterns before implementing new ones.
Don’t: Implement inconsistent accessibility approaches across your application.
Why it works: Consistent accessibility patterns improve overall usability. Having the agent analyze your existing patterns ensures this consistency.
Example:
“Make this drag-and-drop interface accessible for keyboard-only users. First, examine our src/components/ui/ directory to understand our current accessibility patterns and utilities for keyboard navigation, screen readers, and ARIA attributes.”
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Ask the agent to analyze state management across similar components.
Do: Have the agent examine similar existing components before recommending state management approaches.
Don’t: Implement inconsistent state management approaches across similar components.
Why it works: Consistent state management improves maintainability. Having the agent analyze your existing patterns ensures this consistency.
Example:
“What’s the best way to manage form state across these 3 components? Analyze our other multi-step forms in src/components/forms/ to understand our current approach to complex forms and multi-component state management.”
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Have the agent analyze your validation feedback patterns across the UI.
Do: Ask the agent to identify your existing validation feedback patterns before implementing new ones.
Don’t: Implement inconsistent validation feedback across your application.
Why it works: Consistent validation feedback improves usability. Having the agent analyze your existing patterns ensures this consistency.
Example:
“How should we show validation errors for this multi-step form? Analyze our existing forms in src/components/forms/ to understand our error presentation patterns for field-level errors, form-level errors, and validation timing.”
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Ask the agent to analyze responsive design patterns across your application.
Do: Have the agent examine your existing responsive patterns before implementing new ones.
Don’t: Implement inconsistent responsive approaches across your application.
Why it works: Consistent responsive patterns improve maintainability and user experience. Having the agent analyze your existing patterns ensures this consistency.
Example:
“This layout should adapt from 3 columns on desktop to 1 column on mobile. Analyze our src/components/layouts/ and CSS utilities to understand our responsive design approach, breakpoints, and adaptation patterns.”
Advanced Techniques
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Create multi-step workflows with clear file paths for the agent to analyze at each stage.
Do: Break complex workflows into distinct steps with specific files to examine at each stage.
Don’t: Present complex multi-step requests without clear progression and context gathering.
Why it works: Step-by-step workflows with explicit context gathering ensure the agent has all necessary information at each stage.
Example:
“Help me create a dashboard for our application errors. First analyze our error logging in src/utils/logger.js, then examine src/services/monitoring.js to understand our alerting, and finally help create a Grafana dashboard configuration based on these patterns.”
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Create project-specific templates that include file paths for context gathering.
Do: Include placeholders for key files that provide context for common request types.
Don’t: Use templates that focus only on the request without context gathering.
Why it works: Templates with built-in context gathering ensure the agent consistently has the information needed for quality responses.
Example:
“I need to implement a new feature for user notifications. We’re working on [Project Name] using [React, TypeScript, Firebase]. Please first check our [src/components/notifications/, src/hooks/useNotifications.js, src/context/NotificationContext.js] to understand our patterns.”
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Document your codebase’s key patterns in a central file the agent can read.
Do: Create and maintain documentation of key patterns and decision records.
Don’t: Rely on tribal knowledge or scattered comments to convey important conventions.
Why it works: Centralized documentation makes it easy for the agent to understand your standards and patterns with a single reference point.
Example:
“Implement a new feature following our project conventions. Before implementing this feature, please read our CONTRIBUTING.md file which documents our coding conventions, architecture decisions, and testing requirements, then check example files to see how these conventions are applied.”
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Ask for multiple implementation alternatives after the agent has analyzed your codebase.
Do: Have the agent understand your context deeply before presenting alternatives with different tradeoffs.
Don’t: Request alternatives without first ensuring the agent understands your specific constraints.
Why it works: Meaningful alternatives require understanding your specific context. Having the agent analyze your code first ensures practical, relevant options.
Example:
“We have an authentication issue we need to fix. After examining our authentication flow in src/auth/, please suggest both a quick solution we can implement today, and an ideal solution for later that would improve our overall architecture.”
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Provide feedback on what analyses were most helpful and ask the agent to repeat those approaches.
Do: Explicitly tell the agent which analysis approaches were most valuable for your codebase.
Don’t: Silently adapt to the agent’s approach without steering it toward what works best.
Why it works: The agent learns from feedback about which analysis approaches are most valuable for your specific codebase, making each interaction more efficient.
Example:
“That analysis of our state management patterns was extremely helpful. Please do something similar for our routing logic. For this next component, could you use the same approach to examine our src/features/dashboard/ code to identify patterns we should follow? The pattern analysis comparing multiple similar components was particularly insightful.”
As the AI assistant who wrote these tips, I’ve witnessed the entire spectrum of programming interactions. The difference between inefficient and highly effective collaborations often comes down to these seemingly small details in how you structure your requests. My goal is to help you think like a team member who happens to be an AI - with clear communication, shared context, and properly scoped tasks being the foundation for successful development.