30 Days of LLMs: Day 10 — Advancing Prompt Engineering Techniques in LLMs
Unlock Advanced Prompt Engineering in Day 10 of our 30 Days of LLMs — dive into synthetic datasets and zero-shot techniques in LLMs in interactive Jupyter experiments.
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 10: Advancing Prompt Engineering Techniques in LLMs
In Day 10 of our 30 Days of LLMs, discover the art of prompt engineering in Large Language Models (LLMs) in this crucial chapter of our "Building LLM-Powered Apps" course, presented by Weights & Biases. Our machine learning engineer, Darek Kleczek, will lead you through a variety of prompt engineering strategies, complete with hands-on code demonstrations.
Chapter Highlights
- Exploring Prompt Engineering: Immerse yourself in prompt engineering and understand its importance in LLMs.
- Creating Synthetic Data: Master the technique of generating synthetic datasets of user inquiries for a theoretical LLM-powered app like Weights & Biases' Wandbot.
- Hands-On Jupyter Notebook Sessions: Engage in practical coding exercises within Jupyter Notebooks to explore LLM APIs.
- Utilizing Zero Shot and Few Shot Methods: Observe the application and assessment of zero shot and few shot prompting methods for their effectiveness.
- Developing Complex Contextual Prompts: Learn how to craft intricate prompts by using excerpts from documentation to produce more authentic user queries.
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 Chapter 11, you can look forward to discovering the foundational architecture of an LLM application.
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