30 Days of LLMs: Day 3 — W&B Prompts Tracer for LLMs - Deep Dive into Analysis & Tracking
In Day 3 of the Weights & Biases 30 Days of LLMs, we master the W&B Prompts Tracer for in-depth LLM analysis and tracking. Navigate and interpret intricate data views.
Created on December 8|Last edited on December 10
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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 3: W&B Prompts Tracer for LLMs - Deep Dive into Analysis & Tracking
Welcome to the third chapter of our "Building LLM-Powered Apps" series, presented by Weights & Biases! 🚀 This session is centered around the complex aspects of W&B Prompts for Large Language Models (LLMs), particularly emphasizing the effective W&B Prompts Tracer tool.
Chapter Highlights
- Navigating the W&B Prompts Tracer: We invite you to explore the functionalities of the Weights & Biases Prompts Tracer, a tool for tracking and analyzing your LLM operations.
- Overview in Tracer View: Uncover essential information about your recorded sequence in the top table of the Tracer view.
- Insights into Trace Timeline View: Engage with the Trace Timeline view, where selecting different rows will display the full execution trace of your sequence. Investigate various component chains, MLMs, and associated tools.
- In-depth Component Analysis: By selecting specific components, access comprehensive details regarding inputs, outputs, and other data such as token counts.
- Exploring the Model Architecture: Delve into the Model Architecture tab to see the complete setup of your LLM chain.
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
In our next chapter, we will focus on evaluating LLM applications, including identifying areas for improvement and analyzing outcomes.
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