Sentence Transformers v6 makes ColBERT-style retrieval trainable on one consumer GPU. The numbers, two traps in the fine print, and when it beats dense embeddings.
Late interaction embedding models beat dense retrieval by one NDCG point and cost 42x the index. Here is where that trade actually pays off in…
Instead of shipping a 600-item enum to the model, let it invent a fake category and snap it to a real one with embeddings. Cheaper,…
RAG or fine-tuning? One question settles it most of the time. What the benchmarks leave out, where retrieval quietly fails, and the hybrid split I…
My long-context RAG bot kept refusing questions whose answers were right there. The fix was in my anti-hallucination prompt, not my retriever.
pgvector plus HNSW replaced Pinecone on half my side projects. The schema, the index picks, hybrid search with FTS, and where I still reach for…
Real LLM hallucination examples from my own apps, why retrieval doesn't fully fix them, and the cheap code checks I run to catch made-up answers…
After two years of running RAG in production, here are the chunking strategies I actually use, the ones I dropped, and why chunk size is…
Most reasoning LLM failures aren't hallucinations, they're silently skipped steps. Here's what to measure instead of end-to-end answer accuracy.