Retrieval-Augmented Generation (RAG) is how an AI application answers from your documents instead of its training data. The model is only the last step; the system that finds the right passages — the vector database and everything around it — decides whether answers are trustworthy.
The pipeline, end to end
Ingestion loads your sources (PDFs, web pages, tickets, records), cleans them, and splits them into chunks. Each chunk is converted to an embedding — a vector representing its meaning — and stored in a vector database with metadata like source, date, and who may see it.
At question time, the question is embedded the same way, the database returns the most similar chunks, and the model writes an answer using only those passages, ideally with citations back to the source.
Chunking and retrieval quality decide everything
Chunks that are too small lose context; too large and the relevant sentence drowns. Headings, document structure, and overlap between chunks matter more than most model choices. Hybrid search — combining vector similarity with keyword matching — fixes many 'obvious answer missed' failures.
Always add metadata filters for permissions. A RAG system that retrieves documents the user should not see is a data leak with good grammar.
- Structure-aware chunking (headings, sections) over fixed character counts
- Hybrid vector + keyword retrieval for names, codes, and exact phrases
- Permission and freshness metadata on every chunk
- Re-indexing path when source documents change
Choosing the vector database
Hosted vector services are the fastest start. Self-hosted Milvus becomes attractive when data volume, cost at scale, or data-residency requirements grow; it needs real operations — deployment, backups, index tuning, and monitoring.
Choose based on your numbers: vector count, dimensions, query rate, and who will operate it. The database is rarely the bottleneck; retrieval quality is.
Evaluate like it matters, because it does
Create a fixed set of real questions with known-good answers. Score retrieval (did we find the right passage?) separately from generation (did the answer use it correctly?). Re-run the set whenever you change chunking, embeddings, models, or prompts, and keep the failures in a list you actually work through.
Common questions
Even a few dozen well-kept documents can power a useful internal Q&A. Quality and freshness of sources matter far more than volume.
No. Grounding, citations, and evaluation reduce them a lot, and the system should say when it cannot find an answer — but high-stakes outputs still need human review.
Related services
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