Implementation and Evaluation of AI Clustering
- Day: 2025-06-11
- Time: 05:15 to 06:35
- Project: Dev
- Workspace: WP 2: Operational
- Status: Completed
- Priority: MEDIUM
- Assignee: Matías Nehuen Iglesias
- Tags: Ai Clustering, Data Processing, Azureml, Prompt Refinement, Output Evaluation
Description
Session Goal:
The session aimed to implement a data processing pipeline and evaluate AI-generated clustering outputs, particularly focusing on news articles related to US tariffs and the automotive sector in Argentina.
Key Activities:
- Markdown File Metadata Parser: Developed a Python script to parse markdown filenames into structured metadata, facilitating data organization.
- Data Processing Pipeline: Implemented steps to process data using JSONL input, AzureML Flow schema, and Jinja prompts for clustering news articles.
- AI Output Evaluation: Conducted critical analysis and evaluation of AI-generated clustering outputs, specifically on topics like US tariffs and automotive growth in Argentina.
- Prompt Refinement: Improved prompts for analyzing CSV news articles and clustering, ensuring clarity and consistency in outputs.
Achievements:
- Successfully implemented a data processing pipeline using AzureML and JSONL.
- Conducted comprehensive evaluations of AI clustering outputs, identifying strengths and areas for improvement.
- Developed refined prompts for better clustering and deduplication of news articles.
Pending Tasks:
- Further improvements in clustering logic and editorial angles based on evaluation feedback.
- Addressing errors in article ID filtering and ensuring accurate source ID alignment.
Outcome:
The session resulted in a clearer understanding of the strengths and weaknesses of current AI clustering methods and provided actionable insights for future improvements.
Evidence
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