RAG explained: how to make AI answer from your own data
Retrieval-augmented generation (RAG) is a way to make an AI model answer questions from your own data. When someone asks a question, the system first searches your documents for the most relevant passages, then gives those passages to the model with the question so it can answer from them and cite sources. RAG keeps answers current and grounded without retraining the model, and it is the usual approach for internal knowledge assistants and support bots.
What is RAG?
Large language models know a lot, but not your price list, your policies or last week’s project notes. They can also produce confident answers that are wrong. Retrieval-augmented generation solves both problems by adding a search step: before the model answers, the system retrieves the most relevant passages from your own content and includes them in the prompt. The model then answers from that material. The approach was described in a 2020 research paper by Patrick Lewis and colleagues, and it has since become the standard pattern for business AI assistants.
Why use RAG instead of training a model?
For most knowledge questions, RAG is the better fit. Fine-tuning is useful for teaching a model a style or a narrow task, and the two can be combined. Large context windows help too, but sending every document with every question is slow and expensive; retrieval keeps prompts focused.
What are the parts of a RAG system?
| Part | What it does | Common options |
|---|---|---|
| Document loader | Collects content from files, sites and apps | Drive, SharePoint, Notion, help centre, databases |
| Chunker | Splits content into passages | By heading, paragraph or token count |
| Embedding model | Turns text into vectors for semantic search | Models from OpenAI, Google, Cohere and open-source |
| Vector store | Stores and searches embeddings | pgvector, Pinecone, Weaviate, Qdrant |
| Retriever and reranker | Finds and orders the best passages | Hybrid keyword plus vector search, reranking models |
| Language model | Writes the answer from the passages | Claude, GPT, Gemini or open-weight models |
Vector stores are covered in our guide to vector databases.
What is RAG used for?
- Internal knowledge assistants: Answer staff questions from policies, wikis and project files.
- Customer support: Answer customers from help articles, with links.
- Sales enablement: Find case studies, pricing rules and product facts.
- Research: Search across reports and notes with cited answers.
- Compliance: Answer questions from regulations and internal controls.
Where does RAG go wrong?
- Poor chunking splits answers across passages or mixes unrelated topics
- Retrieval misses the right passage, so the model answers from the wrong one
- Outdated or duplicate documents give conflicting answers
- The model adds facts not in the passages; instruct it to say when the answer is not found
- Access control is ignored, so people see content they should not
Most problems are retrieval problems. Improving document quality, chunking and search usually helps more than switching models.
What does a RAG assistant look like in practice?
A 200-person company builds an HR assistant in Microsoft Teams. Its knowledge base holds the employee handbook, leave policy, benefits guide and expense rules, each split into sections by heading. When an employee asks “how many days of paternity leave do I get?”, the system retrieves the matching sections from the leave policy, and the model answers in two sentences with a link to the policy page. If the question is about something the documents do not cover, the assistant says so and suggests contacting HR. The HR team updates a policy by editing the document, and the assistant reflects the change after the next re-index.
How do you improve retrieval?
- Clean the source content: remove duplicates and outdated versions
- Chunk by meaning, such as headings and sections, not fixed lengths alone
- Combine keyword and semantic search for names, codes and numbers
- Add a reranking step to put the best passages first
- Include document titles and section names with each chunk
- Filter results by the user’s permissions
Agents can use RAG as one of their tools, which is part of how AI agent memory works.
How do you measure quality?
- Build a test set: Collect 50 real questions with known correct answers and source documents.
- Measure retrieval: Check whether the right passage appears in the top results.
- Measure answers: Score correctness and whether citations support the answer.
- Fix and re-test: Improve content, chunking or prompts, then re-run the test set.
Not sure whether you need RAG at all? Read fine-tuning vs RAG vs prompting.
For a worked example that pairs RAG with dashboards, read pairing Power BI with an Azure OpenAI assistant and Power BI Q&A vs a custom assistant.
How UIUX Studio helps
We design and build RAG assistants, from content preparation and retrieval to the chat interface and evaluation. Talk to our Agentic AI & Automation and product engineering teams.
Frequently asked questions
What does RAG stand for?
Retrieval-augmented generation.
Does RAG stop AI from making things up?
It reduces it a lot, especially when the model is told to answer only from retrieved passages.
Do I need a vector database?
Usually, though small projects can use simpler search.
Is RAG better than fine-tuning?
For answering from company knowledge, usually yes; fine-tuning suits style or narrow tasks.
How do I keep answers up to date?
Update the source documents and re-index them.