AI / LLM Basics Interview Questions
What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation combines a language model with an external retrieval step, pulling in relevant documents or data before generating a response, rather than relying purely on what the model memorized during training.
- A retrieval step searches an external knowledge source for content relevant to the current query
- That retrieved content is added into the model's context before it generates its answer
- Helps reduce hallucination by grounding responses in retrievable, verifiable source material
RAG is especially useful for keeping answers current or specific to private data, since it lets a model reference information well beyond whatever it happened to see during pretraining.
What happens before generation in a RAG pipeline?
What does RAG help with by grounding answers in retrieved material?
More Related questions...
