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You can bundle multiple files into a single context-rich prompt by attaching source code, docs, and data files together. This creates a richer request for AI models to improve accuracy and reduce back-and-forth.

Prompt bundling combines prompts with relevant project files into a single context-aware request. It is needed for long-running or complex tasks requiring repository context, code samples, and attachments to improve AI accuracy.

Yes, you can maintain stored sessions across tasks and reattach to ongoing investigations. This session-aware workflow allows you to preserve context and continue complex tasks without starting over.

Yes, prompt bundling supports switching between browser and API engines. This engine-choice and delivery feature allows you to select the model type that best suits your specific task and environment.

The best way is to bundle prompts and selected files into a single context-rich request. By attaching source code and documentation, you provide comprehensive repository context that reduces the need for iterative clarification.

While file attachments improve context, limitations depend on the chosen browser or API engine's token limits. For long-running tasks, managing session size is crucial to avoid exceeding the AI model's processing capacity.