RAG is an AI framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information and to give users insight into LLMs’ generative process. 
Retrieval-augmented generation (RAG) is an AI framework for improving the quality of LLM-generated responses by grounding the model on external sources of knowledge to supplement the LLM’s internal representation of information. Implementing RAG in an LLM-based question answering system has two main benefits: It ensures that the model has access to the most current, reliable facts, and that users have access to the model’s sources, ensuring that its claims can be checked for accuracy and ultimately trusted.
Building and Evaluating Advanced RAG Applications - DeepLearning.AI
“In this course, we’ll explore:”
- Two advanced retrieval methods: Sentence-window retrieval and auto-merging retrieval that perform better compared to the baseline RAG pipeline.
- Evaluation and experiment tracking: A way evaluate and iteratively improve your RAG pipeline’s performance.
- The RAG triad: Context Relevance, Groundedness, and Answer Relevance, which are methods to evaluate the relevance and truthfulness of your LLM’s response.
Hands-On RAG guide for personal data with Vespa and LLamaIndex | Vespa Blog