Evaluating Your RAG Solution

Retrieval-Augmented Generation (RAG) is presented as a method for improving LLM factuality and reducing hallucinations by referencing context-specific information. The article provides a detailed, code-based tutorial on building a RAG pipeline using open-source research data, LangChain, and a FAISS vector database. It walks through data ingestion from an API, creating embeddings, and setting up the retrieval mechanism. The guide contrasts a base model's general answer with the more specific response a RAG system can provide, and introduces the concept of evaluating the solution.