📅 2025-11-27 — Session: Automated Review and Correction of Legal Case Summaries
🕒 22:30–23:10
🏷️ Labels: Automation, Data Correction, Ai Improvements, Dataframe Manipulation
📂 Project: Dev
Session Goal
The session aimed to automate the review and correction of legal case summaries, focusing on data extraction and error reduction.
Key Activities
- Developed a systematic approach for reviewing and correcting legal case summaries, including specific verdicts and detected problems.
- Proposed a JSON format for corrected data and established rules for automating the correction process.
- Addressed errors in the automatic ingestion of data for three specific cases, proposing both automatic corrections and necessary human reviews.
- Implemented improvements to AI prompts and schemas to reduce data extraction errors, including clarifying terms, adding confidence indicators, and normalizing formats.
- Expanded and normalized the
person_corelist in DataFrames using pandas, ensuring identifiers are repeated for each entry. - Created a function to flatten nested DataFrames and save them as CSV files, handling both dictionary and JSON string formats.
Achievements
- Successfully outlined a workflow for legal case data correction and ingestion improvement.
- Enhanced AI data extraction processes with improved prompts and schemas.
- Developed robust methods for DataFrame manipulation and CSV export.
Pending Tasks
- Further testing and validation of the automated correction rules and AI prompt improvements.
- Integration of the new DataFrame functions into existing data processing pipelines.