context-optimization

To optimize RAG context, deduplicate retrieved chunks, score their relevance to the query, and filter out low-utility text. This refines raw context into concise, high-signal blocks that fit within your token budget while preserving coverage of all query sub-topics.

Context deduplication detects and removes overlapping passages using semantic similarity or n-gram overlap. By eliminating redundancy in retrieved documents or conversation histories, it ensures every remaining token contributes meaningfully to the AI's task.

Mitigate the lost-in-the-middle effect by applying priority reordering to context chunks. Place the most useful, high-density information at the beginning and end of the context block so the AI model reliably processes the critical details.

Yes, context optimization works for multi-turn agent conversations by refining conversation histories. It filters low-utility dialogue and prioritizes high-density information to maintain concise, relevant context blocks across extended interactions.

The best way to compress retrieved documents is combining relevance scoring with filtering. Assign utility scores combining relevance and information density per token, then discard low-utility chunks while validating coverage of all user query sub-topics.

Avoid automated context filtering when raw inputs are extremely short or lack redundancy, as aggressive filtering might discard necessary nuances. It is designed for large, redundant contexts where token efficiency and relevance scoring are critical.