Advanced LLM Evaluation Techniques: Chapter 22
Skills:
RAG Evaluation90%
๐ค LLM Evaluation Deep Dive - Join as we explore sophisticated evaluation techniques for LLM applications.
๐ง๐พโ๐ Full course with certification and class materials available free at http://wandb.me/building-llm-powered-apps
๐ Daily swag draw and grand prize Airpods draw from Dec 1 and 31, 2023. Details at http://wandb.me/llm-apps-contest
๐ฃ๏ธ Join the course conversation on our Discord channel at http://wandb.me/course-discord
*Episode Description*
In this chapter of our "Building LLM-Powered Apps" course, offered by Weights & Biases, Darek Kleczek, Machine Learning Engineer, guides you through the process of evaluating Large Language Model (LLM) applications. Learn how to implement a model-based evaluation approach using synthetic datasets and understand the importance of tracking data lineage for accurate assessments.
๐ Chapter Highlights
-Implementing an Evaluation Script: Explore the steps involved in setting up an evaluation script for LLM applications.
-Loading and Tracking Evaluation Data: Understand the process of loading evaluation datasets and tracking their versions using Weights & Biases artifacts.
-Using QA Chains for Evaluation: Discover how to utilize conversational retrieval chains for generating model responses to evaluation questions.
-Creating Evaluation Prompts: Learn about constructing effective prompts to evaluate the correctness of LLM-generated answers.
-Analyzing Evaluation Results: Gain insights into calculating model accuracy and logging results for interactive exploration and further analysis.
๐ Enroll for Free: Join us on this educational journey to master the art of building LLM-powered applications. Enroll at http://wandb.me/building-llm-powered-apps.
๐ Next Chapter Sneak Peek: Stay tuned for our course conclusion where we recap key learnings and explore next steps in LLM application development.
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