RAG Systems
Retrieval-Augmented Generation (RAG) grounds a language model in your own data by retrieving the most relevant documents at query time and passing them to the model as context. It is the standard pattern for accurate, source-cited enterprise AI — and in practice, retrieval quality, not the model, decides whether it works.
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RAG is how you make an LLM answer from your knowledge — private, current, and cited — without retraining a model. A pipeline embeds your content into a vector database, retrieves the most relevant chunks for each question, and feeds them to the model.
Retrieval is the hard part
Teams over-invest in prompts and under-invest in retrieval. Chunking strategy, embedding choice, reranking, and evaluation are what move accuracy — the generation step is comparatively easy.
- Chunking — how you split documents shapes what can be retrieved.
- Embeddings + vector store — the semantic memory layer.
- Reranking — a second pass that sharply improves relevance.
- Grounding + citations — force answers to cite retrieved sources to cut hallucination.
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See the breakdownRAG (retrieval-augmented generation) grounds a model in your data at query time and is the right first choice...
See the breakdown