AI Coding Implementation Checklist

Set up your foundation for structured AI-assisted development. These one-time setup tasks create the context AI needs to understand your project.

• Created Business Context document WHY we build: business priorities, strategic objectives, KPIs, trade-off guidelines · template · pack
• Created Functional Context document WHAT it should do: actors, flows, entities, business rules, edge cases · template · pack
• Created Technical Strategy document HOW to build: tech stack, architecture patterns, coding standards, testing requirements · template · pack
• Created Technical Context document WHERE code goes: project structure, file naming, key reference files, integration points · template · pack
• Set up AGENTS.md or CLAUDE.md configuration Entry point that references your four context documents for automatic AI loading · template · pack
• Established code review process for AI code Define review criteria, responsibilities, and what to look for in AI-generated code
• Trained team on structured AI workflows Everyone understands context engineering and specs-driven approach (and avoids vibe coding) · webinar
• Prepared specialized agents (optional) Configure agents for common tasks or team consistency (e.g., backend, frontend, docs) · guide

Before asking AI to generate code, write a task specification that references your existing context documents. Simple bug fixes may need minimal specs; complex features require detailed specifications.

• Described the changes Keep it simple for straightforward tasks, add detail for complex features · guide · template
• Referenced relevant context documents Point AI to existing Business, Functional, Technical Strategy, and Technical Context as needed · guide · template · example
• Defined acceptance criteria Clear, measurable definition of "done" (leverage techniques like BDD) · guide · template · example
• Specified quality requirements Non-functional requirements: performance, security, accessibility, etc. · guide · template
• Identified edge cases and constraints Special cases, boundary conditions, and limitations · guide · template
• Added technical specifications (for complex changes) High-level or low-level design as needed, leverage diagrams as code and reference master specs · guide · template · example

Never blindly accept AI code. Review thoroughly for quality, consistency, and potential issues. Research shows AI generates 76% more code than humans—watch for bloat and scope creep.

• Reviewed for pattern consistency Matches established architectural patterns and coding conventions from Technical Strategy
• Checked for technical debt signals Verbose code, scope creep (features not requested), unnecessary complexity
• Validated against task specification Meets all acceptance criteria without gold-plating or extra features
• Tested edge cases explicitly Not just happy path—verify error handling and boundary conditions
• Updated context documents if needed Document architectural changes, new patterns, or updated business rules in relevant context files
• Verified security considerations No injection vulnerabilities, proper input validation, secure dependencies

Structured AI development requires ongoing refinement. Keep context fresh, measure results, and improve guidelines based on learnings.

• Review and update context documents monthly Keep architecture documentation current as system evolves
• Measure productivity impact Track actual results vs expectations (velocity, code quality, bug rates)
• Monitor technical debt accumulation Regular code quality assessments, watch for code bloat trends
• Refine AI guidelines based on learnings Continuous improvement—update AGENTS.md with better patterns as you discover them