RAG for the enterprise: getting retrieval right
Retrieval-augmented generation lets a model answer from your own documents. When answers disappoint, the cause is usually the retrieval step, not the model.
Clean data beats clever prompts
Duplicate, outdated, or poorly structured documents lead to confident but wrong answers. Before tuning anything else, decide which sources are authoritative, remove stale content, and keep clear ownership of each source.
Chunk with meaning
Splitting documents at arbitrary lengths separates a question from its answer. Chunk by headings and sections, keep useful context such as titles and dates attached, and test different sizes against real questions instead of guessing.
Combine search methods
Semantic search finds related ideas; keyword search finds exact terms such as product codes or policy numbers. Using both, then re-ranking the results, tends to give far better context than either method alone.
Respect access and show sources
People should only get answers drawn from documents they are allowed to see, so permissions must be enforced at retrieval time. Always show the sources behind an answer, so users can verify it and trust grows.