Artificial Intelligence & Next-Gen LLMs
Retrieval-Augmented Generation for Enterprise
Retrieval-augmented generation grounds model answers in your own documents, cutting hallucinations and keeping knowledge current without retraining. A solid enterprise RAG system is mostly data engineering: chunking, embedding, indexing, retrieval, and evaluation. The model is the easy part once the pipeline is sound.
Points clés
- Clean and structure source documents before chunking them.
- Chunk by meaning, not fixed token counts, with overlap.
- Choose an embedding model tested on your domain language.
- Use hybrid search: vector plus keyword for better recall.
- Rerank retrieved chunks before sending them to the model.
- Cite sources in answers so users can verify claims.
- Measure retrieval and answer quality with a labeled eval set.
- Refresh indexes on a schedule and on document change.
Questions fréquentes
How many documents can RAG handle?
Millions with the right index; retrieval quality matters more than raw size.
Does RAG replace fine-tuning?
Often yes for knowledge tasks; fine-tuning helps style and format more than facts.
What causes hallucinations in RAG?
Poor retrieval, weak chunks, or answers that ignore retrieved context.
How do I keep data secure?
Filter by user permissions at retrieval time and log access.
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