📅 2025-04-16 — Session: AI Orchestration and PromptFlow Integration
🕒 21:55–23:30
🏷️ Labels: Ai Development, Promptflow, Backend Integration, Debugging, Modular Design
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance Matías’ understanding and capability in developing a modular AI application, focusing on integrating PromptFlow’s functionalities and addressing backend issues.
Key Activities
- Bridging Knowledge Gaps: Reflected on the skills needed for modular AI app development, identifying strengths and areas for improvement.
- Backend Wiring: Executed integration of a YAML-driven flow runner with a FastAPI backend, including code snippets for endpoints and frontend components.
- PromptFlow Integration: Integrated PromptFlow’s tracing capabilities into the backend, focusing on modular design and efficient flow execution monitoring.
- Strategic Assessment: Conducted a strategic assessment of AI orchestration architecture, identifying alignment opportunities with PromptFlow infrastructure.
- Debugging and Configuration: Addressed OpenAI API key issues in Python, fixed Uvicorn and dotenv issues, and set up a .env file for LLM Flow Engine configuration.
- API Testing: Compiled a cheat sheet for API testing with FastAPI, including tips and shortcuts.
- System Assessment: Provided a detailed assessment of the AI flow system with actionable improvement suggestions.
- Strategic Positioning: Outlined strategies for leveraging PromptFlow while maintaining originality in AI orchestration.
Achievements
- Successfully integrated PromptFlow’s tracing and UI capabilities to improve application observability and debugging.
- Developed a comprehensive understanding of PromptFlow’s SDK versus custom flow engines.
- Established strategic positioning in AI orchestration, emphasizing control and originality.
Pending Tasks
- Further exploration of PromptFlow’s design patterns for batch processing and customization.
- Continued refinement of backend integration strategies and debugging processes.