Enterprise RAG needs more than a vector database
A real company knowledge assistant must handle much more than a few clean PDFs. It may need to answer from policies, work procedures, manuals, Excel files, Word files, scanned documents, screenshots, technical docs, and business system guides. It also needs department-level , , Chinese and English content, frequent document updates, audit logs, answer history, and ways to measure answer quality.
The simple RAG pattern of splitting documents into chunks, storing them in a , retrieving a few matches, and sending them to a language model can break the structure of long documents and miss exact terms like product codes, document numbers, system names, and error codes. The practical direction is better , structure-aware chunking, hybrid search that combines keyword and meaning-based search, reranking, , permission checks, source-based answers, and safe refusal when the evidence is weak. should happen during retrieval, before the model sees any restricted material, because prompt-based permission rules can leak information.
Documents also need and incremental indexing so old or duplicate material does not create wrong answers. A useful system should have an evaluation set from the start and monitor latency, cost, retrieval success, , and failed questions. For structured business data, API or tool calls may work better than forcing everything into documents.
Key points
- The basic “chunk documents, store in a , send top results to a model” pattern is too weak for many enterprise cases.
- Exact terms such as product codes and error codes often need keyword search, not only meaning-based search.
- Hybrid search, reranking, , and citations help make answers more reliable.
- should be enforced before retrieval results reach the language model.
- Evaluation sets and monitoring are needed to track quality, cost, , and failed questions.