iterative-retrieval

You can refine codebase context by using an iterative retrieval pattern that progressively searches and evaluates files based on initial queries and subsequent refinement cycles. This dynamically identifies relevant code for multi-agent workflows.

Subagents lack context because they cannot predict required files upfront, leading to incomplete information. Iterative retrieval solves this by progressively discovering relevant code snippets even in unexpected locations or with unfamiliar terminology.

The best way to implement a dynamic RAG pipeline is through iterative retrieval, which progressively evaluates codebase files to gather relevant context. This ensures efficient token usage and handles unknown context needs in complex exploration tasks.

Yes, iterative retrieval supports efficient token usage by dynamically identifying and evaluating only the relevant codebase files. This progressive refinement cycle ensures agents retrieve specific, necessary context without loading irrelevant information.

You discover relevant code files by initiating an iterative retrieval process with a broad query, then refining the search through subsequent cycles. This dynamically evaluates and narrows down codebase files based on emerging context.

You should not use progressive context refinement for simple, predictable tasks where all required files are already known. The iterative retrieval pattern is specifically designed for complex scenarios with unknown, dynamic context requirements.