Update ai-code-generation-context.md

Author: amburi

patterns/2-structured/ai-code-generation-context.md

@@ -12,24 +12,24 @@ With the growing use of AI tools (like GitHub Copilot, ChatGPT, or custom LLMs),
 
 ## Context
 
- - InnerSource adoption is in place across the organization
- - Developers are encouraged to use AI tools to improve productivity
- - Contributors may not be familiar or failed to prompt meticulously while generating code through AI with the target codebase's idioms, architecture, or constraints
- - Maintainers want to empower contributors using AI while preserving code consistency and maintainability
- - Multiple teams are contributing to shared repositories with varying levels of familiarity with project standards
- - Code review processes are becoming bottlenecked by AI-generated code that requires significant rework
+*  InnerSource adoption is in place across the organization
+*  Developers are encouraged to use AI tools to improve productivity
+*  Contributors may not be familiar or failed to prompt meticulously while generating code through AI with the target codebase's idioms, architecture, or constraints
+*  Maintainers want to empower contributors using AI while preserving code consistency and maintainability
+*  Multiple teams are contributing to shared repositories with varying levels of familiarity with project standards
+*  Code review processes are becoming bottlenecked by AI-generated code that requires significant rework
 
 ## Forces
 
- - **AI Model Limitations**: AI models generate code based on generalized training data, not project-specific patterns, leading to generic solutions that may not fit the project's architecture
- - **Knowledge Gap**: New contributors using AI tools might unknowingly bypass existing architectural norms, coding standards, and established patterns
- - **Review Overhead**: AI-assisted PRs can significantly increase review load if not aligned with existing practices, as maintainers must spend time explaining and correcting deviations
- - **Productivity vs. Quality Trade-off**: While AI tools boost individual productivity, they can reduce overall team productivity if the generated code requires extensive rework
- - **Context Switching Cost**: Developers benefit from AI tools only when they have the right contextual grounding, but manually providing this context for each AI interaction is time-consuming
- - **Inconsistent Standards**: Different AI tools and different prompting approaches by contributors can lead to wildly inconsistent code styles and patterns
- - **Maintenance Burden**: Creating and maintaining comprehensive AI context requires ongoing effort from maintainers
- - **Tool Integration Complexity**: Different AI tools have different ways of consuming context, making it challenging to create universal guidance
- - **AI Tool Cost Constraints**: Comprehensive AI context increases processing costs (AI tools charge based on "tokens"  - units of text measurement) and usage limits, requiring strategic balance between context completeness and efficiency
+*  **AI Model Limitations**: AI models generate code based on generalized training data, not project-specific patterns, leading to generic solutions that may not fit the project's architecture
+*  **Knowledge Gap**: New contributors using AI tools might unknowingly bypass existing architectural norms, coding standards, and established patterns
+*  **Review Overhead**: AI-assisted PRs can significantly increase review load if not aligned with existing practices, as maintainers must spend time explaining and correcting deviations
+*  **Productivity vs. Quality Trade-off**: While AI tools boost individual productivity, they can reduce overall team productivity if the generated code requires extensive rework
+*  **Context Switching Cost**: Developers benefit from AI tools only when they have the right contextual grounding, but manually providing this context for each AI interaction is time-consuming
+*  **Inconsistent Standards**: Different AI tools and different prompting approaches by contributors can lead to wildly inconsistent code styles and patterns
+*  **Maintenance Burden**: Creating and maintaining comprehensive AI context requires ongoing effort from maintainers
+*  **Tool Integration Complexity**: Different AI tools have different ways of consuming context, making it challenging to create universal guidance
+*  **AI Tool Cost Constraints**: Comprehensive AI context increases processing costs (AI tools charge based on "tokens" *  units of text measurement) and usage limits, requiring strategic balance between context completeness and efficiency
 
 ## Solution
 
@@ -42,57 +42,57 @@ Create a `innersource-ai/` folder in the repository root containing:
 #### Core Documentation Files (Required)
 
 `PROMPT.md`: Project-specific instructions for AI tools
- - Naming conventions (variables, functions, classes, files)
- - Logging patterns and error handling approaches
- - Testing strategy and preferred testing frameworks
- - Code formatting and style preferences
- - Common anti-patterns to avoid
- - Preferred libraries and frameworks for specific tasks
+*  Naming conventions (variables, functions, classes, files)
+*  Logging patterns and error handling approaches
+*  Testing strategy and preferred testing frameworks
+*  Code formatting and style preferences
+*  Common anti-patterns to avoid
+*  Preferred libraries and frameworks for specific tasks
 
 `ARCHITECTURE.md`: Lightweight system overview
- - High-level system architecture and component relationships
- - Data flow patterns and API design principles
- - Dependency management guidelines
- - Module organization and layering principles
- - Integration patterns with external systems
+*  High-level system architecture and component relationships
+*  Data flow patterns and API design principles
+*  Dependency management guidelines
+*  Module organization and layering principles
+*  Integration patterns with external systems
 
 `STYLE_GUIDE.md`: Comprehensive coding guidelines
- - Language-specific style rules
- - Code organization patterns
- - Documentation standards
- - Performance considerations
- - Security guidelines and common vulnerabilities to avoid
+*  Language-specific style rules
+*  Code organization patterns
+*  Documentation standards
+*  Performance considerations
+*  Security guidelines and common vulnerabilities to avoid
 
 #### Optional Enhancements (Implement As Needed)
 
 ##### Practical Examples
 
 `EXAMPLES/`: Sample code files demonstrating best practices
- - `good-examples/`: Well-written code snippets with explanations
- - `bad-examples/`: Common mistakes with explanations of why they're problematic
- - `refactoring-examples/`: Before/after code showing proper improvements
- - Template files for common patterns (controllers, services, utilities)
+*  `good-examples/`: Well-written code snippets with explanations
+*  `bad-examples/`: Common mistakes with explanations of why they're problematic
+*  `refactoring-examples/`: Before/after code showing proper improvements
+*  Template files for common patterns (controllers, services, utilities)
 
 ##### Configuration and Tooling
 
 `CONFIG/`: Shared formatter and analysis configurations
- - `.prettierrc`, `.eslintrc`, or equivalent formatting configs
- - `.editorconfig` for consistent editor settings
- - Static analysis tool configurations
- - Pre-commit hook configurations
+*  `.prettierrc`, `.eslintrc`, or equivalent formatting configs
+*  `.editorconfig` for consistent editor settings
+*  Static analysis tool configurations
+*  Pre-commit hook configurations
 
 `INTEGRATION/`: AI tool-specific integration guides
- - GitHub Copilot workspace configuration
- - Custom GPT instructions and prompts
- - IDE plugin configurations
- - CLI tool integration examples
+*  GitHub Copilot workspace configuration
+*  Custom GPT instructions and prompts
+*  IDE plugin configurations
+*  CLI tool integration examples
 
 ##### Advanced Features
 
 `EMBEDDINGS/` (Optional): For advanced LLM integrations
- - Searchable knowledge base of project patterns
- - Vector embeddings of code examples
- - Semantic search capabilities for finding relevant patterns
+*  Searchable knowledge base of project patterns
+*  Vector embeddings of code examples
+*  Semantic search capabilities for finding relevant patterns
 
 ### Implementation Strategy
 
@@ -101,33 +101,33 @@ Create a `innersource-ai/` folder in the repository root containing:
 ### Usage Patterns
 
 #### For Contributors
-1. **Before Starting**: Review the AI context package to understand project standards
-2. **During Development**: Reference specific files when prompting AI tools
-3. **Code Generation**: Include relevant context snippets in AI prompts
-4. **Validation**: Use provided examples to verify AI-generated code aligns with standards
+* **Before Starting**: Review the AI context package to understand project standards
+* **During Development**: Reference specific files when prompting AI tools
+* **Code Generation**: Include relevant context snippets in AI prompts
+* **Validation**: Use provided examples to verify AI-generated code aligns with standards
 
 #### For AI Tool Integration
- - **Direct Reference**: Copy relevant sections into AI tool prompts
- - **URL Integration**: Reference files via repository URLs in AI tools that support web access
- - **IDE Integration**: Configure AI plugins to automatically include context
- - **Custom Workflows**: Integrate context into CI/CD pipelines for automated validation
+*  **Direct Reference**: Copy relevant sections into AI tool prompts
+*  **URL Integration**: Reference files via repository URLs in AI tools that support web access
+*  **IDE Integration**: Configure AI plugins to automatically include context
+*  **Custom Workflows**: Integrate context into CI/CD pipelines for automated validation
 
 ### Maintenance Strategy
 
- - **Version Control**: Track changes to AI context alongside code changes
- - **Regular Updates**: Review and update context as project standards evolve
- - **Community Contribution**: Allow contributors to suggest improvements to AI context
- - **Metrics Tracking**: Monitor the effectiveness of AI context through code review metrics
+*  **Version Control**: Track changes to AI context alongside code changes
+*  **Regular Updates**: Review and update context as project standards evolve
+*  **Community Contribution**: Allow contributors to suggest improvements to AI context
+*  **Metrics Tracking**: Monitor the effectiveness of AI context through code review metrics
 
 ## Resulting Context
 
- - **Improved Code Quality**: AI-assisted contributions become consistent with existing code standards and architectural patterns from the first submission
- - **Reduced Review Friction**: Maintainers can trust incoming PRs more readily, significantly reducing review fatigue and time-to-merge
- - **Enhanced Contributor Experience**: Contributors using AI produce better, more maintainable code even on their first attempts, leading to increased confidence and participation
- - **Scalable Collaboration**: Opens the door to scalable, AI-aware InnerSource collaboration across teams without sacrificing code quality
- - **Knowledge Preservation**: Project knowledge becomes more explicit and accessible, reducing dependency on tribal knowledge
- - **Faster Onboarding**: New contributors can leverage AI tools effectively from day one, reducing the learning curve for project-specific patterns
- - **Consistent Evolution**: As AI tools improve, the context package ensures that enhanced capabilities are channeled toward project-appropriate solutions
+*  **Improved Code Quality**: AI-assisted contributions become consistent with existing code standards and architectural patterns from the first submission
+*  **Reduced Review Friction**: Maintainers can trust incoming PRs more readily, significantly reducing review fatigue and time-to-merge
+*  **Enhanced Contributor Experience**: Contributors using AI produce better, more maintainable code even on their first attempts, leading to increased confidence and participation
+*  **Scalable Collaboration**: Opens the door to scalable, AI-aware InnerSource collaboration across teams without sacrificing code quality
+*  **Knowledge Preservation**: Project knowledge becomes more explicit and accessible, reducing dependency on tribal knowledge
+*  **Faster Onboarding**: New contributors can leverage AI tools effectively from day one, reducing the learning curve for project-specific patterns
+*  **Consistent Evolution**: As AI tools improve, the context package ensures that enhanced capabilities are channeled toward project-appropriate solutions
 
 ## Rationale
 
@@ -143,18 +143,18 @@ To be added.
 
 ## Status
 
- - Structured
- - Drafted in August 2025
+*  Structured
+*  Drafted in August 2025
 
 ## Author
 
 [Amburi Roy](https://www.linkedin.com/in/amburi/)
 
 ## Related Patterns
 
- - [Standard Base Documentation](../base-documentation.md) - Provides the foundation documentation that AI context builds upon
- - [InnerSource Portal](../innersource-portal.md) - Could include AI context availability as searchable metadata for project discovery
- - [Trusted Committer](../trusted-committer.md) - Trusted Committers are responsible for maintaining and evolving the AI context package
+*  [Standard Base Documentation](../base-documentation.md) - Provides the foundation documentation that AI context builds upon
+*  [InnerSource Portal](../innersource-portal.md) -  Could include AI context availability as searchable metadata for project discovery
+*  [Trusted Committer](../trusted-committer.md) -  Trusted Committers are responsible for maintaining and evolving the AI context package
 
 ## License