Why RAG is Important?

Motivation: LLMs have limitations such as fixed training data, hallucinations, and difficulty in handling domain-specific queries.

Advantages of RAG:

• Dynamic knowledge: Accesses updated data from external sources (e.g. web, private docs).

• Lower hallucination risk: Answers are grounded in real documents.

• Flexible and pluggable: You can attach custom knowledge bases.

• Cost-effective updates: No need to retrain the model to integrate new knowledge.

• Improves transparency: It’s possible to show sources of generated content.

When RAG is useful:

• In fast-changing domains like finance or healthcare.

• When explainability and traceability are required.

• When serving enterprise-specific needs with private data.