What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is a technique where a language model's response is grounded in documents retrieved from your own data store at query time. When a user asks a question, the system first searches your document collection for relevant excerpts, then passes those excerpts as context to the language model, which generates an answer based on them. RAG solves the problem that language models are trained on general knowledge and know nothing about your internal documents, procedures, or data.
RAG is a technique that gives a language model access to your own documents at query time, so it can answer questions based on information it was not trained on.
The Problem RAG Solves
When you ask a language model about your company’s return policy, your Q3 sales report, or your internal onboarding process, it has no idea. It was trained on public internet text, not on your documents. It will either admit it does not know, or worse, generate a plausible-sounding answer based on patterns from its training data that may not match your reality. RAG addresses this by connecting the model to your actual documents at query time, so answers are grounded in real content rather than inference.
How RAG Works
The process has two phases. Indexing (done once, then updated as documents change): your documents are split into chunks, each chunk is converted to a numerical vector (embedding) that represents its semantic meaning, and those vectors are stored in a vector database alongside the original text. Retrieval and generation (done on every query): when a user asks a question, the question is also converted to an embedding, the vector database returns the chunks most semantically similar to the question, those chunks are passed as context to the language model along with the question, and the model generates an answer based on what the retrieved chunks say. The model is not guessing; it is reading and synthesising your actual content.
RAG vs Fine-Tuning
Fine-tuning adjusts the model’s weights by training it further on your data. RAG does not change the model: it supplies relevant context at query time. Fine-tuning is appropriate for changing how a model behaves (its tone, its format, its domain-specific vocabulary). It is not a good mechanism for making a model know specific facts from your documents, because factual knowledge injected through fine-tuning is harder to update and less reliable than retrieval. For a question-answering assistant over your internal documents, RAG is the right tool. See building an AI assistant based on your documents for a practical implementation guide.
Practical Examples
A few common business applications: an AI assistant that answers questions about your internal HR policies by searching a PDF collection; a support agent that retrieves relevant sections from a technical manual before answering a customer question; an internal knowledge base search tool that finds relevant past project documents; a contract review tool that retrieves clauses relevant to a specific legal question. In each case, the quality of the answer depends directly on the quality and coverage of the indexed document collection.
When to Choose RAG
Choose RAG when you need a language model to answer questions about your own content: internal documents, product manuals, support knowledge bases, legal contracts, or any other corpus the model was not trained on. RAG is also the right choice when your content changes frequently, because you can add or update documents without retraining anything. For systems where the AI needs to search a knowledge base as part of a larger multi-step task, RAG and AI agents are often used together: the agent decides when to search, and RAG provides the search capability.
Our tip: When setting up RAG, spend most of your effort on chunking strategy and retrieval quality, not on the generation step. A well-configured retrieval step with a basic model outperforms a sophisticated model receiving poor context.
Frequently asked questions
How is RAG different from fine-tuning?
Fine-tuning trains the language model itself on your data, adjusting the model's weights so it 'knows' your content. RAG does not touch the model: it retrieves relevant documents at query time and includes them in the prompt. Fine-tuning is expensive, requires significant data preparation, and produces a static model that needs to be retrained when your documents change. RAG is cheaper, easier to update (add a new document and it is immediately available), and more transparent (you can see exactly which documents were retrieved to generate the answer). For most business use cases, RAG is the better starting point.
What is a vector database and why does RAG use one?
A vector database stores documents as numerical embeddings: high-dimensional vectors that represent the semantic meaning of the text. When a query arrives, it is also converted to a vector, and the database finds the documents whose vectors are most similar to the query vector. This is semantic search: it finds documents that are conceptually related to the query, not just documents that contain the exact same words. Standard keyword search misses synonyms and paraphrases; vector search handles them naturally. Common vector databases include Pinecone, Weaviate, pgvector (a PostgreSQL extension), and Supabase Vector.
What kinds of documents can RAG work with?
RAG works with any text that can be extracted and chunked: PDFs, Word documents, web pages, plain text files, markdown files, database records, email threads, and more. The preprocessing step (parsing, cleaning, chunking, and embedding) is often the most time-consuming part of building a RAG system. Documents with clear structure (headings, paragraphs) chunk better than documents with dense tables or irregular formatting. For scanned PDFs, you need an OCR step before the text can be processed.
How accurate is RAG compared to asking the AI without your documents?
RAG significantly improves accuracy for questions about your specific content compared to querying a model with no context. Without RAG, the model can only use its training knowledge, which does not include your internal documents, and it may generate plausible-sounding but incorrect answers. With RAG, the model's answer is grounded in retrieved text, which you can verify by displaying the source documents. Accuracy depends on the quality of the retrieval step: if the wrong documents are retrieved, the answer will still be wrong. Good chunking, relevant metadata, and testing retrieval quality separately from generation quality are the main levers for improvement.