So far, this tutorial has explored generative models in real-world applications — from large language models and image generation to agents, tool use, retrieval-augmented generation,

So far, we’ve explored models, agents, LLM applications, and their use cases. When moving toward production, however, additional concerns arise: performance, regulatory requirements, scalable deployment,

This chapter covers techniques and best practices for improving LLM reliability and performance in scenarios like complex reasoning and problem-solving. Adapting a model to a

This chapter explores how generative AI can automate and accelerate data science workflows. Large language models (LLMs) are increasingly influential in scientific progress, particularly by

While this tutorial focuses on integrating generative AI—especially large language models (LLMs)—into software applications, this chapter zooms in on using LLMs for software development itself.

This chapter introduces chatbots: what they are, how they evolved, and how modern systems like ChatGPT are built. We focus on state-of-the-art approaches using Large

As LLMs grow more fluent, the challenge is turning that fluency into dependable, capable assistants. This chapter focuses on improving intelligence, productivity, and trustworthiness by

This chapter focuses on setting up everything needed to follow along with tutorial examples. We then introduce several model integrations, including OpenAI’s ChatGPT, Hugging Face

Large Language Models (LLMs) such as GPT-4 excel at generating human-like text, but using them purely through APIs has clear limits. Their real potential emerges

Over the past decade, deep learning has advanced rapidly, enabling machines to process and generate unstructured data such as text, images, and video. These advances—especially