The Engineer’s Framework for LLM & RAG Evaluation

Lesson 8 of the LLM Twin course focuses on evaluating fine-tuned large language models (LLMs) and retrieval-augmented generation (RAG) systems using Opik, an open-source evaluation and monitoring tool by Comet. Participants learn to assess the quality of their LLMs through metrics like heuristics, similarity scores, and LLM judges, which evaluate issues such as hallucination, moderation, and writing style. The lesson emphasizes creating a robust evaluation pipeline to quantify system performance and compares multiple experiments to optimize LLMs for specific tasks. Additionally, the evaluation of RAG systems involves analyzing the interaction between user input, retrieved context, generated output, and expected output using metrics like ContextRecall and ContextPrecision. The lesson highlights the importance of well-crafted prompts for LLM judges and offers insights into improving AI applications by iterating on data collection, cleaning, and hyperparameter tuning, with an emphasis on detailed evaluation processes to guide optimization efforts.

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