RAG Basics
Ground AI in your own documents using retrieval so it answers from your real files instead of guessing.
The problem RAG solves
A model only knows what it learned during training, so it cannot know your company handbook or last week's meeting notes. RAG, which stands for retrieval-augmented generation, fixes this. It lets the AI look up relevant pieces of your own documents and use them to answer. The result is replies grounded in your real information rather than the model's best guess.
How retrieval works
First your documents are split into small chunks and stored in a searchable index. When you ask a question, the system finds the chunks most related to your question and hands them to the model along with your prompt. The model then writes an answer using those chunks. So the model is not memorizing your files; it is reading the most relevant snippets fresh each time you ask.
Why grounding reduces made-up answers
When the model can see the actual text, it is far less likely to invent facts, a problem often called hallucination. Many RAG systems also show citations, the source snippets behind an answer, so you can verify the claim yourself. If the documents do not contain the answer, a well-built RAG setup can say 'I do not find that' instead of guessing.
What RAG does not fix
RAG is only as good as the documents you feed it. If your files are outdated or wrong, the answers will be too. It also will not help if the retrieval step pulls the wrong chunks, so keeping documents clean and well organized matters. RAG grounds the model in your data, but it does not replace good source material or a human sanity check.
Key takeaways
- RAG lets AI answer from your own documents, not just its training.
- Your files are chunked and indexed, then the best chunks feed the model.
- Grounding cuts hallucination, but only if the source documents are good.
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