Implementing Retrieval-Augmented Generation: Moving Beyond Generic Chat
A practical guide to grounding LLM answers in your own data, with the failure modes we see most often.
Retrieval-augmented generation is the most reliable way to make a large language model useful for a specific business. Instead of hoping the model remembers your policies, you retrieve the relevant passages and hand them over with the question.
The architecture is simple on a whiteboard: chunk documents, embed them, store vectors, retrieve the nearest chunks, prompt the model. The hard part is everything around it.
Chunking strategy determines retrieval quality more than the embedding model does. Metadata filters matter as much as similarity. Evaluation sets must exist before the first demo, otherwise every improvement is a guess.
We treat RAG systems as search systems first and language systems second. If retrieval is wrong, no prompt will save you.
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