The resulting functions use the Prompty prompt description to build the interaction with the LLM, which you can wrap in an asynchronous operation. The result is an AI application with very little code beyond assembling user inputs and displaying LLM outputs. Much of the heavy lifting is handled by tools like Semantic Kernel, and by separating the prompt definition from your application, it’s possible to update LLM interactions outside of an application, using the .prompty asset file.
Including Prompty assets in your application is as simple as choosing the orchestrator and automatically generating the code snippets to include the prompt in your application. Only a limited number of orchestrators are supported at present, but this is an open source project, so you can submit additional code generators to support alternative application development toolchains.
That last point is particularly important: Prompty is currently focused on building prompts for cloud-hosted LLMs, but we’re in a shift from large models to smaller, more focused tools, such as Microsoft’s Phi Silica, which are designed to run on neural processing units on personal and edge hardware, and even on phones.