RAG (Retrieval-Augmented Generation)
A technique where the AI retrieves relevant information from a knowledge base before generating a response, reducing hallucinations and grounding outputs in real data.
Why it matters
RAG is how AI answers questions about your specific documents instead of guessing from general training. It matters because it grounds responses in real, retrieved sources, which cuts down hallucinations and lets the AI cite where an answer came from. Most trustworthy "chat with your PDF" and company-knowledge assistants rely on it, and it's why they can stay current without retraining the whole model.
A concrete example
A company help bot using RAG doesn't invent your refund policy. When you ask, it first searches the official policy documents, pulls the relevant paragraph, and then writes its answer based on that exact text, often with a citation. That's the difference between a bot that confidently makes up rules and one you can actually rely on, because its answer is anchored to a real source.
How to use it
This is the right answer whenever the problem is that the model does not know something specific to you: your documentation, your policies, your product data. The quality of a RAG system is mostly decided before the model is involved — how you chunk documents, how you retrieve, and whether the retrieved passages actually contain the answer. When answers are wrong, check what was retrieved before blaming the model. The approach is set out in Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.
The common mistake
Assuming retrieval makes hallucination impossible. If the retrieved passages do not contain the answer, a model will often produce a plausible one anyway. Instructing it to say when the sources do not cover the question, and showing the sources to the reader, is what makes the system trustworthy.
Related terms
Grounding
The process of connecting AI model outputs to verified, real-world information sources. Grounded AI responses cite specific documents, databases, or web sources — reducing hallucinations and increasing factual reliability.
Embedding
A mathematical representation of text (or images) as a vector of numbers that captures meaning. Similar concepts have similar embeddings, enabling semantic search and clustering.
Semantic Search
Search technology that understands the meaning and context of a query, not just keyword matches. Finds 'vehicle maintenance tips' when you search for 'car repair advice.'
AI Dubbing
Automated translation and re-voicing of audio/video content into other languages while preserving the original speaker's voice characteristics, timing, and emotional delivery.
AI Orchestration
Coordinating multiple AI models, tools, and data sources in a unified pipeline. An orchestration layer manages prompt routing, context passing, error handling, and output aggregation across different AI services.
API (Application Programming Interface)
A way for software applications to communicate with each other. AI APIs let developers integrate AI capabilities into their own applications programmatically.