Generate training data and cost-effectively train categorical models with Amazon Bedrock

This article explores using Amazon Bedrock and generative AI to generate high-quality labeled training data for multiclass classification models, specifically focusing on categorizing customer support cases.

• Traditional data labeling is expensive, time-consuming, and prone to class imbalances
• Prompt engineering with XML tags can guide large language models like Claude 3.5 Sonnet to generate accurate labeled datasets
• The approach achieved over 90% accuracy in categorizing support case root causes into six categories:
  • Billing Inquiry
  • Security Awareness
  • Feature Request
  • Software Defect
  • Documentation Improvement
  • Customer Education
• Key steps include:
  • Defining clear category descriptions
  • Providing good and bad example classifications
  • Using XML-structured prompts
  • Iteratively refining the prompt
• The generated labeled data can be used to train machine learning models using AutoGluon

The method offers a cost-effective and accurate alternative to manual data labeling for complex classification tasks.