Overview
- Researchers propose Cluster-based Adaptive Retrieval (CAR), which infers a cut-off from similarity-distance clusters to vary retrieval depth by query complexity.
- The CAR authors report lower token use and latency, fewer hallucinations, and a 200% user-engagement lift after integration into Coinbase’s assistant, though these results are unreviewed.
- A separate preprint details Mycophyto, a domain-tailored RAG pipeline for arbuscular mycorrhizal fungi that combines semantic retrieval with structured extraction of experimental metadata.
- The Mycophyto paper describes storing embeddings in a high-performance vector database to enable near real-time updates from evolving literature.
- A same-day DEV Community explainer outlines RAG’s retriever–store–generator pattern, key tools such as vector databases and frameworks, and challenges including data quality, latency, and breadth-versus-depth trade-offs.