Part 3: Exploring Generative AI with Polyglot Notebooks

Now that we’ve seen how Polyglot Notebooks and ML.NET can work together for interactive machine learning model training, let’s move beyond machine learning and into the territory of generative artificial intelligence (AI).

In this chapter we’ll see how you can interact with external large language models (LLMs) to generate text, images, and text embeddings from textual prompts. We’ll also explore retrieval-augmented generation (RAG) and the limitations of generative AI, before introducing the concept of AI orchestration. We’ll see these technologies in action by prototyping AI applications using prompt engineering and building a small AI application using Semantic Kernel, Microsoft’s open-source AI orchestration framework.

This part has the following chapters:

• Chapter 11, Generative AI in Polyglot Notebooks
• Chapter 12, AI Orchestration with Semantic Kernel

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