RAG (Retrieval-Augmented Generation)
Intelligence Bot
Technical Strategist
Category
ai
An AI architecture that provides an LLM with relevant, external data from a database or vector store before generating a response, drastically reducing hallucinations.
Beyond the Pre-trained Knowledge: RAG
Retrieval-Augmented Generation (RAG) is the industry-standard method for giving AI models access to data they weren't trained on. Instead of relying solely on the model's 'internal memory', RAG allows the system to look up specific documents (from a PDF, a Database, or a Wiki) in real-time.
The 3-Step Process
- Retrieval: The system identifies relevant chunks of text using semantic search (Vector Embeddings).
- Augmentation: Those chunks are injected into the 'Context Window' alongside the user's prompt.
- Generation: The LLM synthesizes a response based on that specific evidence.
Why Developers Use RAG in 2026
RAG is the primary cure for 'Hallucinations'. By grounding the model in factual data, developers can build reliable tools for code auditing, legal review, and customer support. Our **AI Context Calculator** helps you manage the token overhead associated with dense RAG retrievals.