RLM Context Scout

To analyze large data structure without consuming context window, perform structural probing to extract headers and metadata, use strategic sampling for snippets, and build a navigable map for targeted reads.

The best way to map a large codebase before a full read is to run a non-invasive reconnaissance pass that infers the structural skeleton and guides subsequent processing to relevant sections.

Yes, you can extract metadata from large documents without loading full content by enforcing minimal sampling and priors-based searches to locate relevant sections while preserving signal.

Strategic sampling for large context pre-reading works by retrieving representative snippets from massive data dumps, allowing you to infer the document skeleton and plan subsequent targeted reads.

You should use reconnaissance mapping for large data sets when faced with massive documents or data dumps, enabling you to identify relevant sections first and preserve the context window for signal.