Why Large Language Models (LLMs) Misunderstand Queries

Have you ever asked an AI model a coding question, only to receive an irrelevant or incorrect response? This frustrating experience is common among developers. The problem often lies not in the model itself, but in the lack of clear, structured context.

Think of an LLM like a human developer: if you present a question without sufficient details, even an expert might struggle to provide an accurate answer. Proper context is the key to improving responses from AI models.

What is AI-Friendly Documentation?

AI-friendly documentation is structured in a way that maximizes the ability of LLMs to understand and process the information effectively. This involves:

• Presenting information in a clear, coherent format.
• Providing comprehensive, up-to-date data in a way that’s easy for AI to parse.
• Minimizing ambiguity to ensure consistent responses.

My Approach to AI-Friendly Documentation in Angular

When working with Angular, I realized that many developers struggle with receiving relevant help from AI models. This motivated me to experiment with ways to structure documentation for better AI comprehension.

Here’s what I did:

1. Collected AI-Friendly Documentation Examples: By analyzing different formats, I determined what works best.
2. Created a Centralized Knowledge Base: A well-structured, single-file document improves recall and response quality.
3. Tested AI Responses with Different Structures: I assessed how various formats affected LLM-generated answers.

Examples of AI-Friendly Documentation

Some great resources already implementing these principles include:

• Model Context Protocol: A large single-file document containing detailed data.

How You Can Create AI-Friendly Documentation

If you frequently rely on LLMs for coding support, consider structuring documentation in a way that optimizes performance. Follow these best practices:

• Provide clear semantics and context in documentation.
• Use consistent formatting to reduce ambiguity.
• Update documentation regularly to ensure accurate responses.
• Test LLM outputs with different document structures to measure effectiveness.

Conclusion

AI models rely on well-structured context to provide useful responses. By creating AI-friendly documentation, developers can enhance the efficiency and accuracy of LLM interactions. Whether you work with Angular or any other framework, structuring your resources properly will greatly improve your AI-driven development workflow.

Have you experimented with AI-friendly documentation? Share your insights in the comments!