30 Days of LLMs: Day 12 — Data Analysis & Refinement in LLMs
On Day 12 of our 30 Days of Christmas, we dive into dataset analysis and prompt refinement for synthetic Q&A. Harness the power of W&B for realistic data creation. Join us to master LLM dataset optimization.
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 12: Data Analysis & Refinement in LLMs
On Day 12 of the Weights & Biases 30 Days of LLMs, we're continuing with the "Building LLM-Powered Apps" course. Machine learning engineer Darek Kleczek showcases techniques for evaluating and enhancing a synthetic dataset of user queries and responses using the Weights & Biases platform.
Chapter Highlights
- Navigating the Dataset: Discover techniques for effectively using the Weights & Biases dashboard to examine synthetic datasets.
- Evaluating Q&A Combinations: Delve into assessing the quality of synthetic question-answer pairs created by LLMs.
- Enhancing Prompt Crafting: Acquire knowledge about how continuous refinements in prompt crafting can result in more authentic and precise datasets.
- Practical Implications: Learn about the real-life application of this data in developing a question-answering app, relating it to practical scenarios.
- Advanced Evaluation Techniques: Investigate various approaches to further improve and verify the quality of the datasets generated.
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 tomorrow as we start the intriguing task of developing a question-answering application using LLMs.
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