📅 2025-04-15 — Session: Integrating and Evaluating PromptFlow and Prompty
🕒 08:45–10:45
🏷️ Labels: Promptflow, Prompty, Integration, Ai Development, Code Analysis
📂 Project: Dev
Session Goal
The session aimed to explore and integrate Microsoft’s PromptFlow and Prompty tools into existing AI development workflows, focusing on their capabilities and potential benefits for prompt engineering and large language model (LLM) development.
Key Activities
- Analysis of Early OpenAPI Spec: Reviewed foundational ideas of OpenAPI (Swagger) to draw parallels with PromptFlow’s development.
- Insights from Swagger 1.2: Reflected on Swagger 1.2 to inform the Prompt Runtime Contract design.
- Overview of Prompt Engineering Techniques: Provided a comprehensive overview of prompt engineering techniques and their applications.
- Frameworks and Tools Overview: Evaluated various frameworks, including LangChain and Prompty, for prompt engineering.
- Integration and Evaluation of PromptFlow and Prompty: Discussed the integration of PromptFlow and Prompty into Python environments, focusing on their features and community insights.
- Code Analysis and Refactoring: Conducted call graph analysis to identify dead code and improve modularity.
- Coverage and Testing: Addressed issues with Python coverage tools, generating HTML reports for better insights.
Achievements
- Developed a structured plan for integrating PromptFlow and Prompty into existing projects.
- Identified key insights from community feedback and existing frameworks to enhance prompt engineering practices.
- Improved understanding of codebase structure through call graph analysis and coverage reports.
Pending Tasks
- Finalize the integration of PromptFlow and Prompty into the existing project structure.
- Continue refining prompt engineering techniques based on community feedback and tool capabilities.