Retrieval-Augmented Generation (RAG)
Overview
Retrieval-Augmented Generation (RAG) is an approach that enhances Large Language Models (LLMs) by combining them with a retrieval system that fetches relevant information from a knowledge base. This architecture allows the model to access external, up-to-date information during generation, rather than relying solely on its training data.
The key components of RAG include:
- Document Store: A database or vector store containing documents, knowledge articles, or other textual information
- Retriever: A system that finds relevant documents based on the input query
- LLM: The language model that generates responses using both the retrieved information and its own knowledge
The main advantages of RAG include:
- Accuracy: By grounding responses in specific documents, RAG can provide more accurate and verifiable information
- Freshness: The knowledge base can be updated independently of the model, keeping information current
- Transparency: Retrieved documents provide clear sources for the generated content
- Efficiency: Reduces the need to retrain or fine-tune the model with new information
Evaluation
Evaluating RAG systems requires considering multiple aspects beyond traditional language model metrics. The RAGAS framework provides a comprehensive set of metrics specifically designed for assessing RAG systems, including measures for faithfulness, relevancy, and context precision.