Chat With Your Own Documents: RAG in Plain English

You can ask an AI questions about your files, notes or company policies, and get answers drawn from them. The technique is called RAG (retrieval-augmented generation).

1. Collect your documents (PDFs, notes, web pages, wikis).
2. Split them into chunks and index them so they can be searched by meaning.
3. Retrieve the chunks most relevant to your question.
4. Generate an answer from those chunks, ideally with links back to the source.

• Current, private knowledge the model was never trained on.
• Fewer made-up answers, because the model works from supplied text.
• Traceability: you can check the cited passage.
• No retraining needed when documents change.

• Internal policy and HR questions.
• Customer support from product manuals.
• Research libraries and paper collections.
• Personal notes and knowledge bases.
• Contract and report lookup.

• Messy source files: scanned pages, duplicates and outdated versions give bad answers.
• Poor chunking: relevant text split from its context.
• Missing permissions: people seeing documents they shouldn't. Respect access rules.
• Over-trust: answers still need checking for important decisions.

• Clean and date your documents.
• Require citations in answers.
• Let it say "not found."
• Test with questions whose answers you already know.

RAG turns a general AI into one that knows your material. The quality of the answers depends on the quality and organisation of your documents.