Technique

RAG (Retrieval-Augmented Generation)

RAG is a technique that enhances AI responses by retrieving relevant information from external knowledge bases before generating an answer, combining the power of search with generative AI.

What Is RAG?

Retrieval-Augmented Generation (RAG) is an AI architecture pattern that improves the accuracy and relevance of AI responses by first searching a knowledge base for relevant information, then using that information as context when generating an answer. This solves the two biggest problems with pure generative AI: hallucination (making up facts) and stale knowledge (limited to training data).

A RAG system has three components:

  • Knowledge base — Your documents, databases, or content stored as embeddings in a vector database
  • Retrieval — Semantic search that finds the most relevant information for each query
  • Generation — An LLM that synthesizes retrieved information into a coherent response

Why Is RAG Critical for Business AI?

RAG enables AI systems that are grounded in your data — your documentation, policies, product catalogs, and institutional knowledge. Without RAG, AI can only reference what it learned during training. With RAG, it accesses your latest information in real-time. This is the foundation of most enterprise AI deployments, from customer service bots to internal knowledge assistants.

RAGretrieval-augmented generationknowledge base AI

Related Terms

Embeddings

Embeddings are numerical representations of text, images, or other data that capture semantic meaning — enabling AI systems to understand similarity, search, and retrieve relevant information.

Vector Database

A vector database is a specialized database optimized for storing and querying high-dimensional embeddings, enabling fast similarity search that powers RAG, recommendations, and semantic search.

Semantic Search

Semantic search understands the meaning and intent behind queries rather than just matching keywords, using AI to deliver more relevant results from documents, databases, and knowledge bases.

Large Language Model (LLM)

A large language model is an AI system trained on massive text datasets that can understand, generate, and reason about human language — the technology behind ChatGPT, Claude, and Gemini.

Explore Further

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