# Watch the recordings from my Python + AI series

DevFeed: [Watch the recordings from my Python + AI series](<https://devfeed.tech/articles/watch-the-recordings-from-my-python-ai-series-21743.md>)

Original publisher: [Read original article](<http://blog.pamelafox.org/2025/10/watch-recordings-from-my-python-ai.html>)

Author: Pamela Fox (noreply@blogger.com)

Published: 2025-10-31T14:22:00Z

Content type: article

Language: en

Sources: [Pamela Fox](<https://devfeed.tech/sources/pamela-fox.md>)

Topics: [Python](<https://devfeed.tech/topics/python.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Azure OpenAI](<https://devfeed.tech/topics/azure-openai.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [LangChain](<https://devfeed.tech/topics/langchain.md>), [SDK](<https://devfeed.tech/topics/sdk.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [Ollama](<https://devfeed.tech/topics/ollama.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [ollama](<https://devfeed.tech/tags/ollama.md>), [openai](<https://devfeed.tech/tags/openai.md>), [python](<https://devfeed.tech/tags/python.md>), [rag](<https://devfeed.tech/tags/rag.md>)

## AI overview

A blog article provides recordings and materials from a nine-part Python and generative AI series. It covers language models, embeddings, retrieval-augmented generation, evaluation and safety, AI agents, Model Context Protocol, and related Python examples using services including GitHub Models, Ollama, Azure OpenAI, and OpenAI models.

## Source excerpt

My colleague and I just wrapped up a live series on Python + AI, a nine-part journey diving deep into how to use generative AI models from Python. I gave the english streams while my colleague Gwen gave the spanish streams (and I hung out in her live chat, working on my technical spanish!). The series introduced multiple types of models, including LLMs, embedding models, and vision models. We dug into popular techniques like RAG, tool calling, and structured outputs. We assessed AI quality and safety using automated evaluations and red-teaming. Finally, we developed AI agents using popular Python agents frameworks and explored the new Model Context Protocol (MCP). To apply the concepts, we put together code examples that run for free thanks to GitHub Models, a service that provides free models to every GitHub account holder for experimentation and education. The examples are also compatible with local models (via Ollama), Azure OpenAI, or OpenAI.com models. Even if you missed the live series, you can still access all the material using the links below! If you're an instructor, feel free to use the slides and code examples in your own classes. Python + AI: Large Language Models 📺 Watch recording In this session, we explore Large Language Models (LLMs), the models that power ChatGPT and GitHub Copilot. We use Python to interact with LLMs using popular packages like the OpenAI SDK and LangChain. We experiment with prompt engineering and few-shot examples to improve outputs. We also demonstrate how to build a full-stack app powered by LLMs and explain the importance of concurrency and streaming for user-facing AI apps. Slides for this session Code repository with examples: python-openai-demos Python + AI: Vector embeddings 📺 Watch recording In our second session, we dive into a different type of model: the vector embedding model. A vector embedding is a way to encode text or images as an array of floating-point numbers. Vector embeddings enable similarity search across