18 articles tagged with Rag.
Our support bot kept citing half a sentence and missing the answer that sat two lines below. The culprit wasn't the model, it was how we split the docs.
You can't improve retrieval you don't measure. The offline eval harness that lets us change embeddings, chunking, and rerankers with confidence instead of vibes — with the metrics that actually predict production quality.
They solve different problems. RAG injects knowledge; fine-tuning changes behavior. The decision criteria, the hybrid pattern, and what we'd do over.
Pure vector search misses exact-keyword queries. Pure BM25 misses semantic ones. Combining them with reciprocal rank fusion is the simplest large win in RAG retrieval.
Embedding indexes degrade silently. The signals that catch drift, how often to re-embed, and the operational patterns we built after one quiet quality regression.
A working retrieval-augmented generation app you can run today. Markdown ingestion, embeddings, semantic search, and an LLM answer — start to finish in one afternoon.
