Retrieval augmented generation helps language models answer with information from your own documents. The simplest useful version is often enough for internal search, support answers, and knowledge assistants.
Start with Clean Source Material
Collect the documents that users actually need. Remove stale drafts, duplicated pages, and empty boilerplate before generating embeddings.
Chunk for Reading, Not Storage
Chunks should preserve meaning. Split by sections, headings, and paragraphs so each retrieved item can stand on its own.
Use Metadata Early
Add source, date, owner, category, and permission metadata. This makes filtering and citations much easier later.
Evaluate with Real Questions
Build a small test set from actual user questions. Check whether the retriever finds the right context before tuning the language model.
A good RAG system is less about exotic infrastructure and more about reliable documents, useful search, and honest citations.
Frequently Asked Questions
Not always. Small projects can start with a local index or managed search service, then move to a vector database when scale requires it.
Choose one that performs well on your language, domain, and latency requirements. Test with real queries before committing.
Yes. Citations help users verify claims and make debugging much easier.