📅 2025-05-02 — Session: Troubleshooting and Enhancing PromptFlow and Job Funnel Processes
🕒 04:10–04:50
🏷️ Labels: Promptflow, Job Funnel, Automation, Data Validation, Observability
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to troubleshoot and enhance various processes related to PromptFlow and Job Funnel, focusing on data validation, observability, job proposal evaluation, and automation of reporting tasks.
Key Activities
- Troubleshooting PromptFlow Data Mapping Errors: Addressed issues with missing fields in
job_data.jsonlby verifying and correcting the data structure. - PromptFlow Observability: Explored a comprehensive guide for inspecting and auditing observability data, detailing five layers of analysis.
- Job Funnel Run Summary and Proposal Evaluation: Evaluated a job funnel run, including a successful status and recommendations for job proposal submission.
- Generating Informative Post-Run Artifacts: Planned the creation of post-run artifacts for observability and tracking, detailing inputs and outputs for reports.
- Structured Analysis of Job Funnel Input: Analyzed job funnel input instances, outlining structural components and intelligence derived from each stage.
- Job Enrichment Pipeline and Markdown Generation: Developed a pipeline for enriching job entries and generating Markdown reports using Jinja2 templates.
- Transforming JSON Array to JSONL Format: Provided a Python script to convert JSON arrays to JSONL format for use in LLM pipelines.
- Demand Analysis for Automation and AI Roles: Reflected on demand patterns in automation engineering and AI operations, providing strategic insights.
Achievements
- Resolved data mapping errors in PromptFlow.
- Enhanced observability and reporting processes.
- Evaluated and strategized job funnel proposals.
- Developed automation scripts for data transformation.