deep-document-processor

To extract information from large documents without exceeding context limits, use a four-pass structural survey and selective extraction process. This compresses inputs into a token-efficient context artifact, preserving critical structure and constraints for downstream agent reasoning while avoiding naive truncation.

The best way to compress long specifications for agent reasoning is transforming them into decision-optimized context artifacts. This method applies a strict ≤20% compression constraint during selective extraction, ensuring maximum signal retention while prioritizing constraints, dependencies, and counter-intuitive facts.

You need context engineering for research writeups when full document inclusion would waste your context budget or lose critical structure. Specifically, this process targets inputs longer than approximately 2000 tokens to maintain reasoning fidelity and keep context within limits.

Document processing works effectively with codebase documentation by applying a four-pass disciplined extraction. It scans cross-references and conflicts, then assembles a single context artifact that avoids wasted context budget from full inclusion and prevents the loss of structural details.

Naive truncation fails for long papers because it discards decision-relevant structure and critical constraints. Instead of truncating, a four-pass extraction process builds a structural survey and selectively extracts information under a strict token-compression budget to produce a maximum-signal context artifact.

Deep document processing differs from human-oriented summaries by optimizing specifically for agent decision-making. It prioritizes machine-readable constraints and counter-intuitive facts through structural surveys and cross-reference scanning, producing a token-efficient artifact rather than a high-level human narrative.