oracle

To provide contextual code and documentation to an AI for debugging, you bundle prompts with relevant files and project structures. This method enables the AI to understand your specific codebase, delivering targeted refactoring and knowledge retrieval solutions.

The best way to analyze a codebase using AI with browser-based LLMs is to bundle your project files and documentation with your prompts. This provides the AI with necessary contextual understanding for tasks like debugging and code analysis without needing a direct API connection.

You do not strictly need an OpenAI API key to use AI for code analysis, as browser-based LLM interactions are supported. However, an OpenAI API key is optionally required if you want to leverage enhanced API-driven functionality for tasks like refactoring and knowledge retrieval.

Yes, you can preview token counts for files in your src directory before prompting an AI. By using specific CLI tooling designed for prompt engineering, you can evaluate the token size of your bundled code and documentation to ensure it fits within the model's context limits.

Bundling project context with prompts improves AI code suggestions by giving the model direct access to your file structures and documentation. This contextual understanding allows the AI to generate accurate, targeted solutions for complex refactoring and debugging tasks instead of generic responses.