What is Retrieval-Augmented Generation (RAG)?
Retrieval-augmented generation (RAG) is an AI technique that retrieves relevant information from an external source, then feeds it to a language model so the answer is grounded in real, specific data instead of only the model's training.
Retrieval-augmented generation (RAG) is a method for making a language model answer using a specific body of knowledge it was not trained on. Instead of relying only on what the model memorized, RAG first retrieves the most relevant passages from a source you control, then asks the model to generate an answer grounded in those passages.
The result is an AI that can answer questions about your product, your documentation, or your internal processes, with far less guesswork.
Why it matters
Language models are powerful but have two well-known limits: they can confidently make things up (hallucinate), and they do not know anything that happened after training or anything private to your organization. RAG addresses both. By grounding answers in retrieved, up-to-date source material, it reduces hallucinations and lets the model cite current, specific information. It is also cheaper and faster to update than retraining a model, because you change the underlying documents, not the model.
How it works
A typical RAG pipeline has a few stages:
- Index: source content is split into chunks and converted into embeddings (numeric representations of meaning) stored in a vector database.
- Retrieve: when a question comes in, the system finds the chunks most semantically similar to it.
- Augment: those chunks are added to the prompt as context.
- Generate: the language model writes an answer using that context, ideally citing the source.
The quality of a RAG system depends heavily on the quality of the source content. Clean, well-structured documentation retrieves better than messy transcripts or scattered notes. This is where turning videos into structured text helps: Vidocu converts recordings into clear, step-by-step documentation that makes accurate source material for a RAG knowledge base.
RAG and knowledge bases
The most common business use of RAG is a knowledge base assistant that answers questions from a company's help content or internal docs. Because the answers are grounded in real articles, the assistant stays accurate as the content is updated.
Why it matters
Grounds answers in your data
RAG retrieves relevant source passages and feeds them to a language model, so answers reflect your specific content, not just training data.
Reduces hallucinations
By anchoring responses to retrieved material, RAG makes the model far less likely to confidently invent facts.
Cheaper than retraining
You update the underlying documents rather than retraining the model, so knowledge stays current with low effort.
Depends on source quality
Clean, well-structured content retrieves better, which is why turning recordings into structured docs improves RAG accuracy.
Powers knowledge assistants
The most common business use is a knowledge base assistant that answers from help content or internal documentation.
Examples
- •A support assistant that answers customer questions using the company's help center articles.
- •An internal bot that responds to employee questions from SOPs and onboarding docs.
- •A product copilot that cites specific documentation pages when explaining a feature.
- •A search experience that returns a written answer plus links to the source articles.
Frequently asked questions
It means an AI system retrieves relevant information from an external source and uses it to generate an answer, so the response is grounded in specific data rather than only the model's training.
Fine-tuning changes the model's weights by training it on examples. RAG leaves the model unchanged and instead supplies relevant documents at query time. RAG is easier to update and better for frequently changing knowledge.
It reduces them significantly by grounding answers in retrieved source material, but it does not eliminate them entirely. Answer quality still depends on the quality and coverage of the source content.
A vector database stores embeddings, which are numeric representations of meaning. RAG uses it to quickly find the source chunks most semantically similar to a question.
RAG can only retrieve what exists in clean, findable form. Well-structured documentation retrieves more accurately than messy transcripts, so investing in good source content directly improves answers.
Vidocu turns videos into clean, structured documentation with steps and screenshots, creating high-quality source content for a RAG knowledge base, available in 65+ languages.
Related terms
Learn more
- AI Knowledge Base Generator — Create clean, structured source content for a RAG knowledge base.
- Knowledge Center — Centralize the documentation a retrieval system draws from.
- RAG-powered knowledge base (guide) — See how RAG applies to a real knowledge base.
- Video to Documentation — Turn recordings into structured docs that retrieve well.