📅 2025-06-11 — Session: AI Output Evaluation and Prompt Refinement
🕒 05:15–06:33
🏷️ Labels: Ai Evaluation, Prompt Refinement, Data Processing, Article Clustering
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to evaluate AI-generated outputs for clustering news articles and refine prompts for better data processing and analysis.
Key Activities
- Markdown File Metadata Parser: Implemented a Python script to parse markdown filenames into structured metadata and save as JSON lines.
- Data Processing Pipeline: Developed a pipeline using JSONL input with AzureML Flow schema and Jinja prompts for clustering news articles.
- AI Output Evaluation: Conducted a critical analysis of AI-generated outputs related to article clustering on US tariffs, focusing on strengths and weaknesses.
- Prompt Refinement: Improved prompts for analyzing CSV news articles and processing news headlines, emphasizing clustering and deduplication.
- Content Strategy and Analysis: Generated seed ideas for articles and analyzed the automotive sector’s growth in Argentina.
Achievements
- Successfully implemented a metadata parser and data processing pipeline.
- Conducted thorough evaluations of AI outputs, identifying key areas for improvement.
- Refined prompts to enhance clarity and consistency in data processing tasks.
Pending Tasks
- Further improvements in clustering logic and editorial review processes.
- Continued evaluation of AI outputs to enhance topic relevance and source ID accuracy.
Conclusion
The session successfully addressed the evaluation of AI outputs and the refinement of prompts, laying the groundwork for improved data processing and content analysis workflows.