Use case
condense.chat helps RAG pipelines fit more retrieved documents into each request.
Use condense.chat when your retrieval pipeline returns bulky documents, citation-heavy payloads, or too many relevant chunks to fit cleanly into the downstream model window. The product is positioned to preserve retrieval signal while lowering token pressure.
What problem it solves
- Retrieved documents that are too large to forward in full
- Citation-heavy context that competes with working room for the model
- Recall loss caused by dropping chunks too early
- High token cost from sending every retrieved passage verbatim
Why the public site highlights RAG
The landing page explicitly calls out RAG pipelines as a core use case and claims teams can pack roughly three times more chunks into the same window without reranking or giving up recall.
How it works in practice
Teams keep their downstream model and application flow. condense.chat operates as a compression layer before the request reaches the upstream provider, so the retrieval system can stay structurally similar while the payload becomes cheaper and smaller.