Retrieval-augmented generation (RAG)
Retrieval-augmented generation (RAG) is a way to make an AI answer from a chosen set of documents. It first searches those documents for the relevant bits, then writes the answer using only what it found. This cuts down on made-up facts.
In more detail
Normal AI models answer from whatever they picked up during training. That sounds fine until they confidently state something wrong. RAG fixes this by giving the model a specific pile of sources to read first. So instead of guessing from memory, it pulls the right passages and builds the answer on top of them.
Two steps happen each time you ask something. First the system finds the parts of your documents that match your question. Then it hands those parts to the model and says: answer from this. The model becomes more of an assembler of facts than the source of facts, which is why the answers stay closer to the truth.
One thing to watch: RAG is only as good as what it retrieves. If the right passage never gets found, or your documents do not cover the question, the answer can still be thin or wrong. It lowers made-up facts a lot. It does not erase them.
Example
Maya is studying for a biochemistry exam. She asks her AI tool to explain the citric acid cycle. With RAG, it pulls the exact paragraphs from her lecture slides and explains it in those terms, instead of dragging in random details from the wider internet that her professor never taught.
How StudyPDF does this
Bo runs on this idea. You upload your lectures, notes, and PDFs, and Bo only answers from them, with a link to the exact page or second. If something is not in your material, it tells you instead of making it up.
See also
- TermAI hallucination
- TermCited answer
- TermCourse-scoped AI
- TermStudy agent
- ComparisonNotebookLM vs ChatGPT: which is better for studying?
- AlternativesThe best NotebookLM alternatives in 2026
- ComparisonDeepSeek vs ChatGPT: which is better for studying?
- ComparisonCopilot vs Gemini: which is better for studying?
Common questions
Does RAG mean the AI never makes mistakes?
No. RAG lowers made-up facts a lot, but it does not get rid of them. If the search misses the right passage, or your documents simply do not cover the question, the answer can still be off. The fix is good source material and a tool that admits when something is not there.
How is this different from just pasting my notes into a chatbot?
Pasting works for a page or two, then you run out of room. RAG handles a whole course because it searches first and only feeds the relevant bits to the model each time. You also get pointers back to the exact source, so you can check the answer instead of trusting it blindly.
Put it to work on your own course
Upload your lectures and ask Bo. Cited answers, plus flashcards, exams, and more built from your material.