Explored Modular AI Workflows with PromptFlow and RAG

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Explored Modular AI Workflows with PromptFlow and RAG

  • Day: 2025-04-18
  • Time: 18:40 to 19:20
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: In Progress
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: Ai Workflows, Promptflow, RAG, Architecture, Development

Description

Session Goal

The session aimed to explore and understand the modular design of AI workflows, focusing on Microsoft’s PromptFlow and OpenAI’s Retrieval-Augmented Generation (RAG).

Key Activities

  • Discussed the modular approach in AI workflows, particularly Microsoft’s PromptFlow and OpenAI’s RAG, highlighting the roles of various tools and blocks.
  • Reflected on the distinctions and compatibilities between Microsoft’s PromptFlow and OpenAI’s API approaches, focusing on embedding and retrieval tasks.
  • Outlined tools in Microsoft’s PromptFlow for embedding generation, vector storage, and retrieval operations, and their integration with RAG pipelines.
  • Explored the process of enhancing PromptFlow workflows by integrating existing tools and adding custom abstractions to improve UX and modularity.
  • Reviewed a detailed architecture map of PromptFlow, including folder organization related to embedding, vector search, and reusable tools.
  • Outlined the architecture map for the FlowPower/AI Lambda Layer paradigm, detailing core block types and orchestration layers.
  • Explored the architecture and components of a Composable Semantic Runtime for modular AI workflows.

Achievements

  • Completed the README for the FlowPower system, providing a foundational introduction and options for further development.

Pending Tasks

  • Further exploration of the architecture maps and tool package creation for PromptFlow.
  • Implementation of the sprint plan for the FlowPower/AI Lambda Layer paradigm.
  • Development of additional documentation, templates, or starter code for FlowPower.

Evidence

  • source_file=2025-04-18.sessions.jsonl, line_number=8, event_count=0, session_id=cd1495e0bffe70198a75311cd0239e327a00e0bdb6d388866f319446ff44420c
  • event_ids: []

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