The importance of documentation for effective AI-assisted development
Core Idea: While LLMs have broad knowledge of popular frameworks, they require explicit documentation for less common or newer libraries to avoid hallucinations and produce accurate solutions.
Key Elements
- LLMs have likely memorized popular frameworks from their training data
- For less common or post-cutoff technologies, models may hallucinate capabilities
- Manual documentation feeding is necessary when web search isn't available
- Tools like Cursor simplify providing documentation via URL inclusion
- Reading comprehensive documentation remains valuable for understanding full capabilities
Implementation Strategies
- Proactively provide documentation links for specialized libraries
- Include documentation in context for any post-knowledge-cutoff technologies
- Verify LLM-generated code against official documentation
- Maintain a collection of authoritative documentation links for frequent tasks
- Prompt models to explicitly request documentation when uncertain
Documentation Sources
- Official framework/library documentation
- API references
- Tutorials and examples
- Release notes (especially for newer versions)
- Community resources like Stack Overflow (with verification)
Connections
- Related Concepts: Know Your Limits (recognizing when additional information is needed), Requirements Not Solutions (clear expectations)
- Broader Context: Knowledge Management in AI Development (organizing information for AI consumption)
- Applications: Effective LLM Prompting (providing sufficient context)
References
- Edward Z. Yang (2025). "AI Blindspots" collection, March 2025.
#documentation #knowledge-management #ai-development #best-practices
Connections:
Sources:
- From: AI Blindspots