📅 2025-04-18 — Session: Explored Modular AI Workflows with PromptFlow and RAG
🕒 18:40–19:20
🏷️ Labels: Ai Workflows, Promptflow, RAG, Architecture, Development
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore the modular AI workflows using Microsoft’s PromptFlow and OpenAI’s Retrieval-Augmented Generation (RAG).
Key Activities
- Discussed the modular approach to AI workflows, focusing on Microsoft’s PromptFlow and OpenAI’s RAG, detailing the roles of various tools and blocks in these frameworks.
- Explored distinctions between Microsoft’s PromptFlow and OpenAI’s approaches, with a focus on embedding and retrieval tasks.
- Outlined tools available in Microsoft’s PromptFlow for embedding generation, vector storage, and retrieval operations, including integration with RAG pipelines.
- Architected an experience layer on PromptFlow to enhance workflows by integrating existing tools and adding custom abstractions.
- Provided a detailed architecture map of the PromptFlow system, highlighting folder organization related to embedding, vector search, and reusable tools.
- Outlined architecture map for FlowPower/AI Lambda Layer paradigm, detailing core block types, orchestration layers, and observability enhancements.
- Explored the architecture and components of a Composable Semantic Runtime, detailing its modular AI workflow engine and runtime environment.
- Completed the README for the FlowPower system, serving as a foundational introduction.
Achievements
- Gained insights into the modular design of AI workflows and the integration of embedding and retrieval tasks.
- Developed a comprehensive understanding of PromptFlow tools and their applications.
- Completed foundational documentation for the FlowPower system.
Pending Tasks
- Further develop documentation, templates, or starter code for the FlowPower system.
- Explore actionable next steps for tool package creation in PromptFlow.