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You bundle project files with a prompt for AI context by combining natural language requests with specified source code paths. This ensures the AI model receives the necessary project context for accurate code generation and analysis.

To include specific files and exclude test files when generating AI code context, use glob patterns for file inclusion and exclusion. This filters irrelevant files and targets specific source directories for the AI prompt bundle.

Yes, you need a CLI tool installed to bundle prompts and files for LLM processing. The functionality requires a specific command-line interface tool to manage session execution and file bundling operations.

Yes, you can use prompt templating for complex code refactoring and debugging tasks. Bundling detailed refactoring goals with relevant source files enables the AI to provide accurate and context-aware code suggestions.

Yes, AI context bundling works with both API and browser-based execution engines. The system supports multiple execution environments alongside features for session management and prompt templating.

The best way to preview token count for project files before sending context to an AI model is to evaluate the bundled prompt and file payload. This manages context limits and ensures efficient AI processing.