# Fine-tuning Case Studies for Cost Reduction and Performance Improvement ## Overview Airtrain presents two significant case studies demonstrating the practical benefits of LLM fine-tuning for production applications. The presentation focuses on how organizations can achieve substantial cost savings while maintaining or improving model performance through strategic fine-tuning of smaller models. ## Key Fine-tuning Concepts ### When to Consider Fine-tuning - Start with best-in-class models (like GPT-4) for prototyping - Move to fine-tuning only after proving application viability - Consider fine-tuning when facing: ### Prerequisites for Successful Fine-tuning - Well-defined, specific task scope - High-quality training dataset - Robust evaluation harness - Clear metrics for quality assessment ### Data Preparation Best Practices - Remove and fix low-quality data - Eliminate duplicate rows - Use embeddings for similarity detection - Remove outliers - Address underrepresented data - Ensure training data reflects production conditions ## Case Study 1: Healthcare Chatbot ### Challenge - Healthcare company needed patient intake chatbot - Using GPT-3.5 was expensive - Privacy concerns required on-premise deployment ### Solution - Fine-tuned Mistral-7B model - Implemented comprehensive evaluation metrics: ### Results - Achieved performance parity with GPT-3.5 - Maintained high quality scores across metrics - Enabled on-premise deployment for privacy - Significantly reduced operational costs ## Case Study 2: E-commerce Product Classification ### Challenge - E-commerce unicorn processing merchant product descriptions - Needed accurate Google product category classification - GPT-3.5 costs prohibitive at scale - Privacy concerns present ### Solution - Fine-tuned smaller model for specific categorization task - Implemented accurate evaluation metrics - Focused on three-level taxonomy depth ### Results - Improved accuracy from 47% to 94% - Surpassed human accuracy (76%) - Achieved 94% cost reduction compared to GPT-4 - Enabled on-premise deployment ## Cost Analysis Breakdown ### Sample Scenario (100M tokens/month) - GPT-4: $9,000/month - Untuned Mistral-7B: $40/month - Fine-tuned Mistral-7B hosted: $300/month - Self-hosted on GCP L4 GPU: $515/month ## Technical Implementation Considerations ### Infrastructure Options - Cloud API providers for simpler deployment - Self-hosted options using: ### Model Selection Criteria - Start with smallest viable model size - Consider existing tooling ecosystem - Evaluate base model performance - Use comprehensive playground testing - Consider architecture compatibility ### Evaluation Framework - Implement before/after metrics - Use consistent evaluation datasets - Monitor production performance - Enable continuous improvement cycle ## Best Practices for Production ### Monitoring and Maintenance - Continuous quality monitoring - Regular model retraining - Data lifecycle management - Performance tracking ### Deployment Strategies - Consider hybrid approaches - Balance cost vs. complexity - Plan for scaling - Implement proper security measures The case studies demonstrate that with proper preparation and implementation, fine-tuning smaller models can achieve comparable performance to larger models while significantly reducing costs. The key is having high-quality training data, clear evaluation metrics, and a well-defined specific use case.