30 Days of LLMs: Day 21 —Advanced LLM App Evaluation
For Day 21 of the W&B 30 Days of LLMs, we focus on evaluating and optimizing Large Language Models. Learn effective strategies and the role of user feedback in LLM app enhancement.
Created on December 10|Last edited on December 22
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We'll be taking a day-by-day look at our Building LLM-Powered Applications course — and giving you the chance to win some great prizes!
30 Days of LLMs Contest
By enrolling in our free Building LLM-Powered Applications course, you will automatically be entered into a prize draw to win the coveted W&B socks. Complete the course, and you'll be entered into the draw to win a pair of Apple AirPods Pro!
Day 21: Advanced LLM App Evaluation
Welcome to Day 21 of our complimentary course, "Building LLM-Powered Apps," brought to you by Weights & Biases. In this segment, Darek Kleczek, a Machine Learning Engineer at W&B, guides us through the essential process of improving and fine-tuning Large Language Model (LLM) applications using efficient evaluation methods.
Chapter Highlights
- Significance of Efficient Evaluation: Grasp the importance of assessing LLM applications for their betterment and fine-tuning.
- Challenges in Assessing LLMs: Uncover the complexities involved in the evaluation of LLM applications, considering their unpredictable and complex outputs.
- Methods for Automated Evaluation: Delve into automated evaluation techniques, utilizing datasets with predefined answers, to streamline the assessment process.
- Detailed Evaluation Techniques: Investigate the detailed evaluation strategy for an in-depth and comprehensive analysis of LLM responses.
- Incorporating User Feedback for Assessment: Observe the role of user inputs, such as thumbs up/down responses, in gauging the effectiveness of LLM applications in practical situations.
- Managing Versions with Weights & Biases: Understand the use of Weights & Biases artifacts for version management and performance monitoring in LLM applications.
Key Course Information
- No deep machine learning knowledge is needed, just some familiarity with Python programming.
- Strategies for continual enhancement of your LLM applications.
- Unique perspectives on the LLM tools used by Weights & Biases.
Free Enrollment
Preview of the Next Chapter
Be sure to catch tomorrow's chapter, focusing on hands-on methods for the enhancement and optimization of LLM applications.
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