📅 2025-05-02 — Session: Resolved PromptFlow data mapping and observability issues
🕒 04:10–04:50
🏷️ Labels: Promptflow, Troubleshooting, Data Mapping, Observability, Job Funnel
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to troubleshoot and resolve data mapping errors in PromptFlow, improve observability, and analyze job funnel processes.
Key Activities
- Troubleshooting PromptFlow Data Mapping Errors: Addressed issues related to missing fields in the
job_data.jsonlfile by following a structured troubleshooting guide. - PromptFlow Observability: Reviewed a comprehensive guide to enhance data inspection and auditing capabilities within PromptFlow.
- Job Funnel Analysis: Evaluated a job funnel run, assessed job proposals, and identified strategic tensions in job filtering and recommendations.
- Post-Run Artifacts: Planned and outlined the creation of post-run artifacts for improved observability and tracking.
- Job Enrichment and Markdown Generation: Developed a pipeline for job enrichment and Markdown report generation using Jinja2 templates.
- Data Transformation: Implemented a Python script to convert JSON arrays to JSONL format, facilitating integration with LLM pipelines.
Achievements
- Successfully resolved data mapping errors and improved observability in PromptFlow.
- Conducted a detailed analysis of job funnel inputs and proposed actionable recommendations.
- Developed a scalable pipeline for job enrichment and report generation.
Pending Tasks
- Further refine the job funnel analysis and proposal evaluation process to align with strategic goals.
- Continue monitoring demand patterns in automation and AI roles for strategic positioning.