engineering · 1 min read
Contextual Compression: Filtering Retrieved Chunks Before Generation
Most retrieved chunks are noisy. Contextual compression filters them down to what is actually relevant - saving tokens and lifting answer quality.
BABhogar AI TeamProduct & Engineering
Even with great retrieval, most retrieved chunks contain irrelevant content alongside the relevant signal. Stuffing all of it into the LLM context burns tokens and dilutes attention.
Why it matters
Contextual compression solves this with a small extractor model that pulls only the relevant sentences from each retrieved chunk. The LLM sees less noise, generates better answers, and costs less per call.
How Bhogar AI approaches it
Bhogar AI ships compression as a per-KB option. The compression model runs after retrieval and before generation; the resulting compressed context is what the answer model sees and what citations point to.
- Sentence-level compression with relevance scoring
- Configurable compression ratio per KB
- Citations preserved through compression
- Compatible with parent-document retrieval
- Per-KB cost-per-query telemetry
What you get
Customers using compression cut tokens-per-answer 30-50% with measurable lift on faithfulness scores.