AI BriefingCompressing RAG Context to Only What's Needed for the Actual Answer
Key point
This introduces a technique that uses a small LLM to remove 68% of RAG context while maintaining 96% recall.
Details
In RAG (Retrieval-Augmented Generation) pipelines, a significant portion of the numerous chunks fetched by the retriever is noise that isn't actually necessary for generating the answer. This noise leads to increased cost and wasted context.
To solve this, Kapa added a Pruning step using a small LLM between the retriever and the generation model (Generator). The key achievements of this approach are as follows.
- Cost reduction: Lowered cost per query to about 1/3 the level
- Efficiency: Removes 68% of the context while maintaining 96% recall
- Accuracy: Adopts a method that grasps the relationships between chunks, rather than a simple Rerank score-based cutoff
Existing Rerankers use a Pointwise approach that scores each chunk individually, which has the limitation of not reflecting the interrelationships between chunks or their importance within the overall context. Kapa overcomes this by selecting only the information essential to the answer, freeing up the agent's context space and maximizing reasoning efficiency.
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