30 Days of LLMs: Day 23 - LLM App Enhancement Strategies
Welcome to Day 23 of the W&B 30 Days of LLMs! Join as we explore setting up evaluation scripts, using QA chains, and analyzing results for accuracy and improvement
Created on December 11|Last edited on December 22
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Day 23: LLM App Enhancement Strategies
Welcome to Day 23 of our complimentary course, "Building LLM-Powered Apps". In this segment, our expert Darek Kleczek, Machine Learning Engineer at W&B, navigates us through the intricacies of assessing Large Language Model (LLM) applications. Discover the methodology of conducting model-centric evaluations using artificially created datasets, and grasp the critical role of maintaining data origin records for precise evaluation.
Chapter Highlights
- Crafting an Assessment Script: Delve into the methodology of creating a script to assess LLM applications.
- Managing and Monitoring Assessment Data: Learn the techniques for importing assessment data sets and keeping track of their iterations with Weights & Biases tools.
- Employing QA Sequences in Assessments: Uncover the approach to using dialogue-based retrieval sequences to produce model responses for assessment queries.
- Developing Assessment Prompts: Acquire knowledge on formulating impactful prompts to judge the accuracy of responses generated by LLMs.
- Examining Assessment Outcomes: Understand the process of determining model precision and documenting findings for dynamic examination and detailed analysis.
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.
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Preview of the Next Chapter
Join us for tomorrow's instalment where we'll revisit essential concepts and delve into the future steps for developing LLM applications.
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