The development of Azure AI Studio involves a fundamental change in the way we use AI models. Instead of simply making an API call to a single model, we’re now building pipelines that mix different aspects of a model, or even chaining different models to deliver a multimodal application. Tools like LangChain, Semantic Kernel, and Prompt Flow are now essential frameworks for taming and controlling the output of generative AI, grounding it in our own data.

For example, we can have a computer vision application that identifies objects in a picture, feeding that list into a generative AI large language model to produce a text description of the image, before using a voice generator to read that description to a visually impaired user holding a camera.

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