30 Days of LLMs: Day 22 — LLM App Enhancement Strategies
For Day 22 of the W&B 30 Days of LLMs, we advance in the art of enhancing LLM apps and learn error analysis, prompt engineering, and document search optimization for your LLMs.
Created on December 11|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
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Day 22: LLM App Enhancement Strategies
Welcome to Day 22 of our complimentary course, "Building LLM-Powered Apps," brought to you by Weights & Biases. Darek Kleczek, a Machine Learning Engineer at W&B, shares key tactics for improving and refining Large Language Model (LLM) applications. This session will concentrate on pinpointing areas for enhancement and applying practical fixes.
Chapter Highlights
- Pinpointing Weak Spots: Discover how to scrutinize your app's design to identify areas that require upgrades.
- Advanced Error Analysis: Learn about utilizing tools like W&B Tracer for error evaluation, which helps in precise advancements.
- Enhancing Document Retrieval: Uncover different techniques to boost document search efficiency, including specialized embedding models and keyword search strategies.
- Refining Prompt Engineering: Investigate sophisticated prompt engineering methods for steering LLMs toward specific outcomes.
- Exploring Various LLMs: Evaluate the advantages of testing different LLM providers and their models.
- Optimizing Cost and User Experience: Grasp the balance between advanced approaches, expenditure, and user waiting periods in LLM-based apps.
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
Join us for tomorrow's instalment, focusing on proficient evaluation methods to gauge enhancements in LLM applications.
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