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Product Title Optimiser

Use cases

Restructuring messy supplier titles at scale Standardising title format across categories Improving title keyword prominence Validating no product info is lost during optimisation

Uses GPT-4o (or local LLM via custom endpoint) to restructure product titles by category.

Creates templates from up to 50 titles per category, then batch processes (default 20 per request).

Validates all numbers from original appear in optimized version and maintains minimum 80% word overlap.

UK English spelling normalisation.

Requires API Key

Platform

Python script (requires Python 3.x)

Input

Product titles CSV

OpenAI API key

Output

CSV with original and optimised titles

View Source

Features

  • GPT-4o with local LLM endpoint support
  • Category-based template creation (up to 50 titles)
  • Batch processing (default 20 per request)
  • Numerical validation (all numbers preserved)
  • 80% minimum word overlap verification
  • Missing words flagging in output
  • Rate limit handling with exponential backoff

How to use

  1. 1 Prepare CSV with Name and Categories columns
  2. 2 Run with --input, --model, --batch-size options
  3. 3 Review output with Optimized Title, Is Same, Missing Words columns
  4. 4 Check flagged titles where words were lost

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