What Is RAG? The Key Technology to Reduce AI Hallucinations
Learn about RAG (Retrieval-Augmented Generation), how it works, and why enterprises are adopting it to build reliable AI systems.
What Is RAG?
RAG (Retrieval-Augmented Generation) is a technique where an LLM retrieves relevant information from an external knowledge base before generating a response. This significantly reduces AI hallucinations and enables accurate answers that reflect up-to-date information.
Why Is RAG Necessary?
LLMs have inherent limitations:
- Knowledge cutoff: They don't know information after their training data
- Hallucinations: They can generate plausible but incorrect information
- Lack of domain knowledge: They don't have access to internal company documents
RAG is an architecture designed to overcome these limitations.
How RAG Works
RAG operates in three main stages:
Stage 1: Indexing
Documents are split into small chunks, each chunk is converted into a vector embedding, and stored in a vector database.
Stage 2: Retrieval
The user's question is converted into a vector, and the most semantically similar document chunks are retrieved from the vector DB.
Stage 3: Generation
The retrieved documents are included as context and passed to the LLM, which generates a response based on this information.
RAG vs Fine-tuning
| Aspect | RAG | Fine-tuning |
|---|---|---|
| Knowledge updates | Just add/modify documents | Requires retraining |
| Cost | Relatively affordable | High GPU costs |
| Transparency | Source tracking possible | Sources unclear |
| Deployment speed | Fast | Slow |
Enterprise RAG Use Cases
- Customer support: Providing accurate answers based on internal manuals and FAQs
- Legal research: Searching case law and statute databases to assist legal counsel
- Medical diagnosis support: Referencing latest medical papers for diagnostic information
- Internal knowledge management: Searching company documents to answer employee questions
2026 RAG Trends
RAG technology is becoming increasingly sophisticated:
- Agentic RAG: AI agents dynamically decide search strategies as needed
- Graph RAG: Structured retrieval using knowledge graphs
- Multimodal RAG: Including images, tables, and charts as search targets beyond text
- Self-RAG: LLMs autonomously judge the need for retrieval and verify search results
RAG has established itself as the most practical and effective approach for enterprise AI adoption, and is expected to continue evolving as a core technology.