Browsing: Rag

There’s a category error that shows up in many LLM systems: We retrieve a few documents, put them in context, generate a fluent answer with links, and then we call the answer “grounded.”

I built the whole thing in pure Python and ran the tests myself, with the results shown as pass or fail.

Benjamin Nweke described a failure that took him three weeks to notice. He had turned on Structured Outputs for a pipeline that parsed payment confirmation messages into transaction records, and his reconciliation job started flagging a small, steady stream of mismatches — a couple of percent of a week’s volume. Not crashes. Not malformed rows.…

Retrieval-augmented generation (RAG) answers questions using information retrievedfrom a set of documents. That document collection is called a corpus. A typical evaluationsends a well-written question to a tidy corpusand checks whether the retriever returns the correctpassage.

Retrieval-Augmented Generation (RAG) is the most widely used LLM use case across organizations. By vectorizing documents and retrieving semantically similar chunks at query time, RAG mitigates hallucinations, grounds responses and bypasses static knowledge cutoffs imposed by model pretraining. However, in a real-life scenario, standard vector-based RAG runs into limitations for complex queries, such as those…