Retrieval-augmented generation (RAG) is the most practical way to make language models useful for enterprise applications. The idea is simple: retrieve relevant documents from your knowledge base and feed them into the prompt. Simple in theory. Brutally hard to get right in practice.
Why RAG beats fine-tuning for most use cases
RAG naturally supports source attribution: every response can point back to the exact document and passage it drew from. This is critical for enterprise use cases where trust and verifiability matter.
When the model's knowledge is grounded in retrieved documents, it is much less likely to confabulate. And when it does, the mismatch between the generated answer and the source documents is easily detectable.
Third, hallucination control. RAG doesn't eliminate hallucination, but it constrains it. When the model generates its answer given retrieved documents, it's much less likely to confabulate, and when it does, the mismatch between the answer and the source documents is detectable.
The RAG architecture stack
Document processing
Ingesting enterprise documents isn't just about throwing PDFs at an API. You need to handle tables, headers, footnotes, multi-column layouts, scanned images, and metadata extraction.
Embedding & indexing
Document chunks are converted into vector embeddings and stored in a vector database. Model selection matters: multilingual embedding models perform differently across languages.
Retrieval strategy
Naive vector similarity search is a starting point, not a solution. Production systems need hybrid retrieval, re-ranking with cross-encoders, and query expansion for wider recall.
Generation & grounding
The LLM receives the retrieved context and generates a response. Prompt engineering ensures the model stays grounded, cites sources, and states when it doesn't have sufficient information.
Where RAG systems break
If you split a document in the wrong places, cutting paragraphs in half or separating headers from their content, the retrieval step will return useless fragments.
Embedding model mismatch. Using an English-optimized embedding model for Dutch legal documents will give poor retrieval quality. We've seen dramatic improvements from multilingual or domain-adapted models.
Stale indices. If your vector database isn't synchronized with the source documents, users get answers based on outdated information. This is worse than no answer at all.
What enterprise RAG gets right
The best RAG systems handle multi-format ingestion, support incremental index updates, provide clear source attribution, and include feedback loops for continuous improvement.
Perhaps most importantly, they're honest about their limitations. A system that says "I don't have enough information to answer this" is infinitely more useful than one that confidently generates plausible-sounding nonsense.
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