Creating Traceable LLM Workflows with Promptflow, Prompty, and OpenAI

In this guide, we will construct a comprehensive LLM workflow utilizing Promptflow within a Google Colab environment. Our journey begins with the installation of a keyring backend to mitigate OS dependency issues while securely configuring our connection to OpenAI. Next, we will set up a dedicated workspace and create a structured Prompty file, which serves as the primary LLM component of our workflow. We will then develop a class-based flow that integrates deterministic preprocessing with LLM reasoning, enabling the infusion of computed hints into the model's responses. Additionally, we will implement tracing to keep track of each execution step, allowing for both single and batch queries, and produce outputs in a well-organized format. To conclude, we will enhance the system with an evaluation pipeline that employs an LLM-as-a-judge to assess responses against expected outcomes.