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Data & Vector Databases

RAG Explained: Retrieval-Augmented Generation for Business AI

RAG combines LLMs with retrieval from your own data. Foundation of enterprise AI knowledge systems.

RAG (Retrieval-Augmented Generation) combines LLMs with retrieval from external data sources. Foundation of enterprise AI knowledge applications.

How RAG works

  • User asks question
  • System retrieves relevant documents from knowledge base
  • LLM generates answer using retrieved context
  • Response includes citations to source documents
Versus pure LLM: RAG has access to your specific data. Reduces hallucination, enables current information.

Tools

  • Vector databases: Pinecone, Weaviate, Chroma, Qdrant
  • Embedding models: OpenAI, Cohere, Anthropic, open source
  • RAG frameworks: LangChain, LlamaIndex
  • Enterprise platforms: Glean, Microsoft Viva, Salesforce Einstein

When to use RAG vs fine-tuning vs prompting

RAG: Need current data, frequent updates, verifiable sources. Most enterprise use cases.

Fine-tuning: Need specific style or behavior changes. Less common.

Prompting alone: Simple, no special knowledge needed.

Bottom line

RAG is dominant pattern for enterprise AI knowledge applications in 2026.

Frequently asked questions

What is RAG?

Retrieval-Augmented Generation. LLM combined with retrieval from external data. Enables AI to answer using your specific data with citations.

Why RAG over fine-tuning?

Easier to update (just update data), better citations, lower cost. Most enterprise use cases prefer RAG. Fine-tuning still relevant for specific style/behavior.

Best vector database?

Pinecone managed dominant. Open source options (Weaviate, Chroma, Qdrant) growing. Cloud platforms (AWS, Azure, GCP) increasingly include vector. Choice based on scale and integration.

When does RAG not work?

When data quality is poor, when retrieval is poor, when LLM ignores retrieved context. Quality of each component matters.

Building RAG systems?

LangChain or LlamaIndex frameworks accessible. Plus vector database and LLM. Many enterprises building internal RAG over knowledge bases.

Related guides

LLMs & ModelsLarge Language Models Explained for Business 2026read guideAI AgentsAI Agents Explained: What They Are and How Business Uses Themread guideBuilding with AIPrompt Engineering: The Practical Guide for 2026read guideAI for Enterprise B2BAI for Enterprise Knowledge Managementread guide

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