I’ve heard that RAG is used to improve LLMs. How does it work, and what problems does it solve?
sathishb89@gmail.com Answered question
Retrieval‑Augmented Generation (RAG) combines a language model with an external knowledge base. When asked a question, the system first retrieves relevant documents from a database, then uses the LLM to generate an answer grounded in those documents. This approach solves two major issues:
- Hallucinations – LLMs sometimes invent facts. RAG reduces this by anchoring responses in real data.
- Knowledge freshness – LLMs are trained on static datasets. RAG allows them to access updated information without retraining. For example, a medical chatbot using RAG can pull the latest research papers before answering, ensuring accuracy. It’s widely used in enterprise AI to provide reliable, domain‑specific answers.
sathishb89@gmail.com Answered question