Evaluate RAG with LLM Evals and Benchmarks

The text discusses Retrieval Augmented Generation (RAG), a technique that enhances the output of robust language models by leveraging external knowledge bases. RAG involves five key stages: loading, indexing, storing, querying, and evaluation. The text also covers how to build a RAG pipeline using LlamaIndex and Phoenix, a tool for evaluating large language model performance. The pipeline is evaluated using metrics such as NDCG, precision, and hit rate, which measure the effectiveness of retrieving relevant documents. Additionally, the text discusses response evaluation, including QA correctness, hallucinations, and toxicity. The evaluations provide insights into the RAG system's performance, highlighting areas for improvement.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.