Grounding AI in Your Own Knowledge (RAG)
Why models invent answers about your business, and how retrieval-augmented generation fixes it by giving them your actual sources.
Chunking and source hygiene
Most RAG failures are content failures When a grounded assistant gives bad answers, the retrieval step is usually fine — the content it was pointed at was the problem. Two things dominate: how documents are split, and what is in the knowledge base at all. Chunking Documents are split into chunks before embedding, because you […]
Keeping the corpus honest
A grounded system is only as good as what it is grounded in RAG answers faithfully from your documents. If your documents contain three versions of the expenses policy, two of them obsolete, the system will answer faithfully from the wrong one — and it will cite its source while doing it, which makes the […]
When not to build RAG
The most expensive RAG system is the one that should have been a search box RAG is genuinely the right answer for a large body of documents and open-ended questions. It is also proposed constantly for problems it does not fit, at considerable cost. Four cases where something simpler wins The corpus is small and […]