Introduction: Welcome to the final episode of our Intro to Generative AI series! In this episode, Daniel Whitenack takes the concepts you’ve been learning and shows you how to apply advanced techniques like message chaining and factuality scoring to make your AI-driven systems smarter and more reliable. This session will help you understand how to create workflows that combine multiple models, ensuring your AI can provide accurate, context-aware responses and make decisions grounded in real data.

Introduction: In Episode 6 of the Intro to Generative A.I. series, Daniel shifts the focus from basic search techniques to more dynamic, on-the-fly AI applications. He demonstrates how to enhance AI-driven interactions by integrating real-time data retrieval and multi-turn conversations, pushing beyond static data sources to create more responsive and context-aware systems. Implementing real-time parsing and AI search of live websites. Enhancing chatbots with the ability to handle and respond to ongoing conversations.

Introduction: Welcome to Episode 5 of our Intro to Generative AI series! In this episode, Daniel explores practical techniques for enhancing AI models’ ability to handle large volumes of text data effectively. He addresses the challenges developers face when working with extensive content, such as entire web pages or internal documents, and provides actionable strategies to optimize the retrieval and processing of relevant information. Context Handling: Splitting large text into manageable chunks while preserving context.

Introduction: Welcome to Episode 4 of our Intro to Generative AI series! In this episode, Daniel dives into the essential technique of prompt engineering, focusing on creating dynamic and interactive prompts to enhance the capabilities of AI models. Prompt Templating: Techniques for creating and using dynamic prompt templates to enhance AI interactions. Context Management: Strategies for integrating and switching between multiple contexts in AI applications. Interactive Systems: Building AI systems that respond to user inputs in real-time, using terminal input loops and command-line arguments.

Introduction: Welcome to Episode 3 of our Intro to Generative AI series! In this episode, Daniel explores the critical aspect of prompt formatting when working with AI models, emphasizing how proper formatting can significantly enhance the quality of the AI’s responses and overall performance. Prompt Formatting: Learn the importance of using specific prompt formats for AI models to enhance response quality. API Convenience: Discover how chat completions endpoints simplify prompt formatting and improve integration.

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.

Introduction: Welcome to the first episode of our “Intro to Generative A.I” series! In this episode, Daniel dives into the intriguing world of large language models (LLMs), providing a comprehensive understanding of how these powerful tools work and their practical applications. Gain insights into the architecture and functionality of large language models like Llama 3, and how they process and generate language-based responses. Learn how these models can be integrated into Go projects, enhancing capabilities like autocomplete, code generation, and more through practical examples and demonstrations.