Evaluate RAG with LLM Evals and Benchmarking

The workshop "RAG Time! Evaluate RAG with LLM Evals and Benchmarking" by Arize AI provided valuable insights into Retrieval Augmented Generation (RAG) and its applications. RAG enhances the output of robust language models by leveraging external knowledge bases, ensuring more accurate and relevant responses. The five key stages in building a RAG pipeline are loading data, indexing, storing, querying, and evaluating. A code-along exercise was provided to build a RAG pipeline using LlamaIndex and Phoenix Evals for large language model evaluation. The code-along exercise demonstrated how to install libraries, import them, launch the Phoenix application, download, load, and build an index, query the index, evaluate the results, compute NCDG and precision at 2, log evaluations to Phoenix, and perform response evaluation. The RAG pipeline was evaluated using Phoenix LLM evals, demonstrating its retrieval performance and QA correctness. The evaluation results showed that the system is not perfect but can generate correct responses ~91% of the time with a Hallucinations score of 0.05.

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