Advancing Prompt Engineering Techniques in LLMs: Chapter 10

🤖 Unlock Advanced Prompt Engineering in Chapter 10: Dive into synthetic datasets and zero-shot techniques in LLMs.

*Episode Description*
Unlock the potential of prompt engineering in Large Language Models (LLMs) with this vital chapter of our "Building LLM-Powered Apps" course, brought to you by Weights & Biases. Darek Kleczek, our expert machine learning engineer, guides you through various prompt engineering techniques with practical code experiments.

🌟 Chapter Highlights
-Understanding Prompt Engineering: Delve into the world of prompt engineering and its significance in LLMs.
-Synthetic Dataset Generation: Learn how to generate a synthetic dataset of user questions for a hypothetical LLM-powered application like Weights & Biases' Wandbot.
-Interactive Experiments with Jupyter Notebooks: Follow along with interactive coding sessions in Jupyter Notebooks to experiment with LLM APIs.
-Exploring Zero Shot and Few Shot Techniques: See how zero shot and few shot prompting techniques can be applied and evaluated for effectiveness.
-Advanced Contextual Prompts: Discover how to create more complex prompts using documentation excerpts to generate more natural user questions.

👉 Next Chapter Sneak Peek: Stay tuned for our next chapter, where we focus on building the baseline architecture for an LLM application.