What Is RAG? Retrieval-Augmented Generation — 2026 Enterprise Guide

Short definition

RAG augments an LLM by retrieving relevant chunks from an external knowledge source (docs, DB, API) and passing them as context before generation. One line: RAG = Search + LLM.

Why

LLMs hallucinate, are stale, and don't know your enterprise data. RAG fixes all three.

Architecture

Query → embedding → vector DB → top-k chunks → prompt with context → LLM → answer with citations.

Components

Document loader, chunking, embedding, vector DB (pgvector, Pinecone, Qdrant, Chroma, Weaviate), retriever (hybrid + rerank), LLM, orchestration (LangChain, LlamaIndex, or no framework).

Use cases

Internal assistants, customer support, sales enablement, legal/compliance, engineering Q&A, finance, healthcare.

Limits

Source quality, chunking sensitivity, privacy, sync, conflicting sources, multi-hop reasoning.

RAG + Agent

Agents plan + use tools; RAG becomes one tool. See what is an AI agent.

2026 best practices

pgvector, hybrid search, reranking, metadata filtering, Ragas evaluation, guardrails, semantic cache, observability.

RAG vs fine-tuning

Changing knowledge + citations → RAG. Style/terminology → fine-tune. Both → hybrid.

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*Makrops delivers custom RAG chatbots, document assistants and AI agents with OpenAI, Anthropic, Google and on-prem Llama/Mistral. 6-12 week MVPs. AI service or contact.*