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…
What I like about MCP servers: they give me lots of great tools that can make my agents more powerful, with very little work on my side. 🎉 What I don't like about MCP servers: they give me TOO many tools! I usually only need a handful of tools for a task, but a server can expose dozens. 😿 The problems with too many tools: LLM confusion. The LLM will be presented with the tool definition for every single tool in the server, and it needs to decide which tool (if any) is the best for the job. That's a hard decision for an LLM - it's always better to make it easier for the LLM by narrowing the…
I do not consider myself an expert in generative AI, but I now know enough to build full-stack web applications on top of generative AI models, evaluate the quality of those applications, and decide whether new models or frameworks will be useful. These are the resources that I personally used for getting up to speed with generative AI. AI foundation Let's start first with the long-form content: books and videos that gave me a more solid foundation. AI Engineering By Chip Huyen This book is a fantastic high-level overview of the AI Engineering industry from an experienced ML researcher. I…
OpenAI released the GPT-5 model family today, with an emphasis on accurate tool calling and reduced hallucinations. For those of us working on RAG (Retrieval-Augmented Generation), it's particularly exciting to see a model specifically trained to reduce hallucination. There are five variants in the family: gpt-5 gpt-5-mini gpt-5-nano gpt-5-chat: Not a reasoning model, optimized for chat applications gpt-5-pro: Only available in ChatGPT, not via the API As soon as GPT-5 models were available in Azure AI Foundry, I deployed them and evaluated them inside our popular open source RAG template. I…
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When we develop user-facing applications that are powered by LLMs, we're taking on a big risk that the LLM may produce output that is unsafe in some way - like responses that encourage violence, hate speech, or self-harm. How can we be confident that a troll won't get our app to say something horrid? We could throw a few questions at it while manually testing, like "how do I make a bomb?", but that's only scratching the surface. Malicious users have gone to far greater lengths to manipulate LLMs into responding in ways that we definitely don't want happening in domain-specific user…
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For Pycon 2025, I created a poster exploring vector embedding models, which you can download at full-size. In this post, I'll translate that poster into words. Vector embeddings A vector embedding is a mapping from an input (like a word, list of words, or image) into a list of floating point numbers. That list of numbers represents that input in the multidimensional embedding space of the model. We refer to the length of the list as its dimensions, so a list with 1024 numbers would have 1024 dimensions. Embedding models Each embedding model has its own dimension length, allowed input types,…
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Update: The approach has slightly changed (in a good way!). Read this Microsoft Learn article for an updated guide. This year, we're seeing the rise in "reasoning models", models that include an additional thinking process in order to generate their answer. Reasoning models can produce more accurate answers and can answer more complex questions. Some of those models, like o1 and o3, do the reasoning behind the scenes and only report how many tokens it took them (quite a few!). The DeepSeek-R1 model is interesting because it reveals its reasoning process along the way. When we can see the…
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The azure-search-openai-demo repository was first created in March 2023 and is now the most popular RAG sample solution for Azure. Since the world of generative AI changes so rapidly, we've made many upgrades to its underlying packages and technologies over the past two years. But we've never changed the default GPT model used for the RAG flow: gpt-35-turbo. Why, when there are new models that are cheaper and reportedly better, such as gpt-4o-mini? Well, changing the model is one of the most significant changes you can make to impact RAG answer quality, and I did not want to make the change…
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