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.
- Learn how to maintain conversation context in AI systems for more accurate, relevant responses.
- Implement a factuality score to ensure AI outputs are grounded in real, verifiable data.
- Understand how to chain multiple models together to create complex, multi-step AI processes for enhanced functionality.
Daniel starts by introducing message chaining, a technique that lets you keep track of conversations by appending user and assistant messages to a message thread. This enables your AI to maintain a continuous stream of context throughout the conversation. You no longer have to worry about your AI forgetting earlier parts of the discussion; instead, it will use the history of the chat to deliver more relevant and accurate answers as the conversation progresses. This is particularly valuable in complex interactions like customer support or consultation systems, where maintaining continuity is essential for a smooth user experience. By leveraging message chaining, you’re building smarter, more interactive chatbots that can handle multi-step queries seamlessly.
In the next part, Daniel explains how to integrate a factuality score into your AI system,
allowing it to validate responses by comparing the generated answers with a source of truth, such as external data or verified documents. This is especially critical when you’re using AI in domains that require high levels of accuracy, such as healthcare, legal matters, or finance. By having a model that can check itself and report how “factual” its answers are, you’re ensuring that the system provides more trustworthy outputs. Daniel goes a step further by showing how you can chain multiple models to create complex workflows. Whether it’s translating text, summarizing emails, or automating multi-step tasks, this episode gives you the tools to create dynamic, multi-faceted AI processes that deliver robust, reliable results. By the end, you’ll have learned how to build AI systems that not only interact but also think and validate like an expert, improving both performance and accuracy in real-world applications.
Things you will learn in this video:
How to Maintain Context in AI Conversations: You’ll learn how to chain user and assistant messages together to ensure your AI responses remain relevant across multiple interactions.
Using Factuality Scoring for Accuracy: Discover how to incorporate a factuality score into your AI model to verify that responses are aligned with trusted data sources.
Building Complex AI Workflows: Gain insight into chaining AI models together to handle complex tasks, such as summarization, translation, and report generation, improving your AI system’s versatility.
Video