Refactored AI Processing Pipeline

2 min read

📅 2025-02-23 — Session: Refactored AI Processing Pipeline

🕒 00:20–04:50
🏷️ Labels: AI, Modularity, Error Handling, Literature Review, Pipeline, Python
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The primary objective of this session was to enhance the AI processing pipeline by improving modularity, error handling, and the integration of query text into AI prompts.

Key Activities

  • Literature Review Synthesis Instructions: Developed a structured approach for curating and synthesizing literature excerpts into a coherent academic review.
  • Alternative AI Query Implementation: Implemented an alternative AI query function with improved error handling.
  • Function Naming Best Practices: Established clear naming conventions for AI query functions to enhance code readability.
  • Improved AI Query Functions: Updated the query_ai_freeform function to handle JSON parsing issues and log errors effectively.
  • Understanding Abstraction Elevation: Reflected on software design principles to improve productivity and modularity.
  • Modularization Strategy: Developed a strategy for modularizing the AI processing pipeline into distinct components.
  • Refactored Python Script: Organized the AI processing pipeline into well-defined modules for better maintainability.
  • Modifying AI Processing Pipeline: Adjusted the pipeline to include query_text for more relevant AI responses.
  • Streamlined AI Prompt for Academic Review: Designed a refined prompt to improve AI-generated literature reviews.

Achievements

  • Successfully refactored the AI processing pipeline to improve modularity and readability.
  • Enhanced error handling and logging in AI query functions.
  • Integrated query text into the AI processing pipeline to enhance the relevance of AI responses.

Pending Tasks

  • Further testing and validation of the new modular components.
  • Continuous improvement of AI prompts based on academic feedback.

Graph View

Backlinks