What is the core idea behind RAG?
The model looks up the answer before writing it.
How do RAG differ from related concepts?
| Concept | Difference |
|---|---|
| RAG vs Pure LLM | LLMs rely on training data. RAG retrieves external information |
| RAG vs Fine-tuning | Fine-tuning changes the model. RAG supplements it with data |
| RAG vs Search | Search returns documents. RAG generates answers from them |
How do RAG work?
- User asks a question
- The system retrieves relevant documents from a knowledge base
- Retrieved documents are passed to the language model as context
- The model generates a response grounded in the retrieved information
What are the limitations of RAG?
- Poor retrieval quality leads to poor answers
- Irrelevant documents can confuse the model
- Does not eliminate hallucinations entirely
Why are RAG important?
RAG has become the standard architecture for enterprise AI because it reduces hallucinations, keeps responses current, and allows AI to access proprietary or recent information not in its training data.
How are RAG used in practice?
Used in enterprise chatbots, customer support, internal knowledge management, legal research, and healthcare. Key components include embedding models, vector databases, and language models. Popular frameworks include LangChain, LlamaIndex, and Haystack.
Frequently Asked Questions
Why is RAG better than just using a language model?
RAG grounds the model's responses in actual documents, reducing hallucinations and enabling access to information beyond the model's training data, including proprietary and recent information.
Does RAG eliminate hallucinations?
RAG significantly reduces hallucinations but does not eliminate them entirely. The model can still misinterpret or misrepresent retrieved information.