one sentence answer
Retrieval-Augmented Generation is often referred to as RAG. It combines "finding information" and "generating answers" to make AI closer to your documents, knowledge base or corporate information.
Why you need RAG
If you're asking about internal company SOPs, course materials, customer FAQs, or product manuals, the model itself doesn't necessarily know that. RAG will first find relevant fragments from the specified data, and then give it to the model to organize the answers.
Suitable for the scene
Enterprise knowledge base
Make it easier for employees to access policies, procedures, product information and FAQs.
Curriculum and Learning
Answer questions based on your own notes, handouts, and book excerpts.
customer support
Generate reply suggestions based on reviewed FAQs and help documents.
limitations
RAG is not a panacea. If the material is out of date, written vaguely, or key passages cannot be retrieved, the answer may still be incomplete.
continue learning
After understanding hallucination and context window, it will be easier to know what problems RAG solves and what problems remain unresolved.