/2 min
Why RAG fails in production — and where
When RAG breaks, the model usually isn't the problem — retrieval is, about 73% of the time. Here's the real failure map and what to fix first.
Tag
#retrieval
8 posts.
/4 min
Why Dense Retrieval Fails on Acronyms, IDs, and Code (and How Hybrid Fixes It)
Vector search misses exact terms like SKUs and error codes because embeddings blur rare tokens; a sparse channel restores exact-match signal.
/3 min/reference
Metadata Is the Unsung Hero of RAG Accuracy: What to Extract and How to Filter
Attach structural, content, and contextual metadata to chunks, then use it for filtered multi-axis retrieval to sharpen RAG accuracy.
/4 min/reference
Matryoshka Embeddings in Production: One Vector, Many Sizes
Matryoshka Representation Learning packs meaning into early dimensions so you can truncate one embedding to many sizes for fast, cheap retrieval.
/2 min/reference
Hybrid search for RAG: combining BM25 and dense vectors with RRF
Why you can't average keyword and vector scores — and how Reciprocal Rank Fusion combines them for an 8–15% accuracy gain.
/3 min/reference
How to choose chunk size and overlap for RAG
Start at 512 tokens with light overlap, then tune by query type. A practical, benchmark-backed guide to chunk size for retrieval.
/3 min/reference
Corrective RAG (CRAG) Explained: Self-Healing Retrieval Without Fine-Tuning
Corrective RAG inserts a retrieval evaluator between vector search and the LLM that scores documents and routes to proceed, filter, or re-retrieve.
/4 min/reference
Chunk Size and Overlap for RAG: A Reference with Sensible Defaults
Use structure-defined chunks first; when you must set a size, 256-512 tokens with 10-20% overlap is a sane default, then tune.