What is Retrieval Augmented Generation (RAG)? 10 Types & Use Cases
Retrieval Augmented Generation (RAG) is an AI approach that combines retrieval (searching relevant data) with generation (LLMs producing answers). Instead of relying only on training data, RAG pulls external knowledge to improve accuracy.
How RAG Works (3 Steps)
- Retrieve → Get relevant documents from a database
- Augment → Add context to the prompt
- Generate → LLM produces the final answer
10 Types of RAG
1. Simple RAG
Basic retrieval + generation pipeline.
Use case: FAQ bots
2. RAG with Memory
Adds conversation memory for better context.
Use case: Chatbots
3. Branched RAG
Retrieves from multiple sources and merges results.
Use case: Enterprise search
4. HyDE RAG
Uses hypothetical queries to improve retrieval.
Use case: Research queries
5. Adaptive RAG
Dynamically selects retrieval strategy.
Use case: Smart assistants
6. Corrective RAG
Fixes retrieved results before generation.
Use case: Medical/legal AI
7. Self-RAG
Model evaluates and improves its own outputs.
Use case: Coding assistants
8. Agentic RAG
Multi-agent system for complex workflows.
Use case: Autonomous AI
9. Multimodal RAG
Works across text, images, and video.
Use case: Media search
10. Graph RAG
Uses knowledge graphs for deeper reasoning.
Use case: Scientific research
RAG in Production
RAG is widely used to build reliable AI systems by grounding outputs in real data, reducing hallucinations and improving accuracy.
- Customer support bots
- Enterprise AI assistants
- AI copilots
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