What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation (RAG) is a technique where a system first retrieves relevant passages from your own documents, then has a large language model answer using only those passages, so responses stay grounded in your data.
How It Works
RAG puts a search step in front of a language model. A question comes in, the system finds the passages in your documents most likely to contain the answer, and the model is told to answer using only that text. A RAG pipeline runs in five stages: ingest the files, chunk them into pieces, embed each chunk as a vector, retrieve the chunks closest to the question, and generate the answer. For example, ask a contract chatbot about a renewal clause and it pulls the matching paragraphs before answering. When the goal is answering from your own documents, RAG is easier and cheaper to update than fine-tuning, because adding knowledge means uploading a file rather than retraining a model.
Go deeper: AI automation services · RAG pipelines explained, stage by stage · ROI calculator
Key Benefits
- Answers grounded in your own documents
- Easier and cheaper to update than fine-tuning
- Source passages can be shown next to every answer
- Reduces hallucinations, though it does not eliminate them
- New knowledge goes live by uploading a file
Common Use Cases
- Internal knowledge-base chatbots for staff
- Answering questions about contracts and policies
- Customer support answers drawn from product documentation
- Search across PDFs, spreadsheets and slide decks
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Related Terms
Workflow Automation
The use of technology to automate repeatable business processes, reducing manual intervention and ensuring tasks are completed consistently and efficiently.
AI Agents
Autonomous software programs powered by artificial intelligence that can perceive their environment, make decisions, and take actions to accomplish specific goals.
Large Language Model (LLM)
A large language model (LLM) is a neural network trained on very large text collections to predict the next token, which lets it understand and generate human language for tasks like answering, summarizing and drafting.
Vector Database
A vector database stores embeddings, numeric vectors that capture the meaning of text or other data, and returns the nearest neighbors to a query by similarity using approximate nearest neighbor search.
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