Artificial Intelligence
A simple LLM can generate answers. But a RAG system can generate accurate answers based on your own data.
What is RAG?
Retrieval-Augmented Generation (RAG) combines two systems:
- Retriever ? finds relevant documents
- Generator ? generates final answer
How RAG works
Documents ? Embeddings ? Vector DB
User Query ? Embedding ? Similarity Search ? LLM ? Answer
Why RAG is important
- Reduces hallucinations
- Provides up-to-date information
- Works with private data
- Improves enterprise AI reliability
Tools used in RAG tutorial
- Sentence Transformers
- FAISS / Qdrant
- LangChain
Conclusion
If you're building AI assistants, learning RAG is essential. It turns a basic chatbot into a knowledge-aware AI system.
