AI Text Processing Pipeline

Overview

I'm developing an intelligent text processing solution that automates the extraction of valuable data from thousands of unstructured text files in the high-end luxury product market. Rather than crafting and maintaining custom parsers for each data source—a process that becomes unwieldy as sources evolve and formats vary—I've engineered a sophisticated prompt system that harnesses the power of ChatGPT to transform raw text into structured JSON data.

This approach offers remarkable adaptability in the face of inconsistent data sources and evolving formats, reducing both development overhead and maintenance complexity. By delegating the pattern recognition and extraction tasks to an LLM through carefully calibrated prompts, the system can identify and extract relevant information with minimal human intervention, saving days of work per data source.

Technologies Used

Python for application architecture and data orchestration
ChatGPT API for natural language understanding and data extraction
JSON for structured data representation
Advanced caching strategies for parallel processing and API rate limit management
Workflow pipeline architecture (similar to Temporal.io) for reliable processing
Database technologies for persistent storage and retrieval
Custom prompt engineering for reliable extraction patterns

My Role

As the architect and lead developer of this project, I am:

Designing the end-to-end data processing architecture
Implementing robust error handling and fallback mechanisms
Developing and refining the prompt engineering to ensure consistent extraction
Creating validation systems to verify data quality and completeness
Building scalable processes to handle growing volumes of text
Optimizing parallel processing workflows with intelligent caching

Challenges and Solutions

Working with AI-powered text extraction presents several interesting challenges:

Prompt Engineering: Crafting precise instructions that consistently yield the correct data structure across varied inputs
Model Variation Handling: Building resilience against API and model changes over time
Inconsistent Source Data: Implementing adaptive approaches to handle missing fields and format inconsistencies
Validation Mechanisms: Cross-referencing data across multiple sources to ensure accuracy
Scale Processing: Managing the extraction of data from 30+ distinct sources efficiently
Rate Limiting: Implementing sophisticated caching to optimize API usage while maintaining throughput

Project Scale

The initial phase of this project involves processing:

Thousands of unique luxury products
Approximately 20,000 product images
Roughly 2,500 product listings
Data from more than 30 distinct sources

The system is designed for continuous expansion, with plans to incorporate additional data sources and enhance existing product profiles over time.

Outcomes

As an ongoing project (initiated in March 2025), the current achievements include:

A functional data ingestion and processing pipeline
Reliable JSON transformation of unstructured text
Comprehensive product profiles that aggregate information across multiple sources
Scalable database architecture for the processed data

Next Milestones

Once the data extraction and structuring pipeline reaches production stability:

Develop an intuitive front-end interface to visualize comprehensive product profiles
Create systems to track product history, including previous listings and ownership changes
Implement comprehensive user management with authentication
Deploy flexible monetization options including subscription models and single-purchase access
Add engagement features such as referral systems and promotional discounting