Introduction:

Welcome to Episode 2 of our Intro to Generative AI series! In this segment, Daniel dives into the practical aspects of working with large language models (LLMs) using the Go programming language and the Prediction Guard API.

• Accessing LLMs: Learn how to set up and connect to hosted models using the Go client for Prediction Guard.
• Prompt Engineering: Discover how to create effective prompts and configure parameters like max tokens and temperature.
• Output Variability: Understand how to manage and utilize variability in AI responses for different results.

He begins by introducing the newly released Go client for Prediction Guard, explaining how it facilitates seamless connections to hosted models. This allows developers to leverage powerful LLMs without needing specialized hardware, providing a more accessible and efficient way to incorporate advanced AI capabilities into their projects.

Daniel then transitions into the critical topic of prompt engineering. He demonstrates how to create effective prompts and configure essential parameters such as max tokens and temperature to control the variability and length of the generated text. By adjusting these settings, developers can fine-tune the output to meet their specific needs, ensuring they receive precise and relevant responses from the models. Throughout the episode, Daniel provides clear, step-by-step examples to illustrate these concepts in action.

Finally, Daniel explores the concept of output variability, emphasizing its importance in generating diverse AI responses. He explains how setting parameters like temperature can influence the results and highlights the implications of running the same prompt multiple times to obtain different outputs. This segment provides valuable insights into managing and utilizing the inherent variability of LLMs, enabling developers to harness their full potential. Whether you’re an experienced Go developer or new to generative AI, this episode equips you with the knowledge and tools to effectively integrate LLMs into your projects, enhancing both functionality and innovation.

Things you will learn in this video

• Set up and use the Go client for Prediction Guard to access hosted language models.
• Explore how to use parameters to customize the behavior and output of the language models for specific needs.
• Understand how to manage and utilize output variability through temperature settings.

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