Creating Your Own LLM Classification System

Introduction

• Presenter: Dave Abar
• Background: Founder of Data Lumina, 5 years of experience in custom data and AI solutions
• Goal: Help viewers build their own LLM classification systems in 5 steps

Context and Objectives

• Example: Classifying customer care tickets
• General Applicability: Techniques can classify text, images, audio, etc.
• Problem Definition: Classify customer care tickets to categorize them and add relevant metadata
• Challenges:
  • No structured outputs initially
  • No validation leading to inconsistent categorization
  • Limited information extracted
  • No confidence score
  • Model creativity generating incorrect outputs

Step-by-Step Guide

Step 1: Define Objectives

• Objectives:
  • Accurate category classification
  • Assess urgency and sentiment
  • Extract key information for resolution
  • Confidence score for human review
• Business Impact:
  • Reduce average response time
  • Improve customer satisfaction
  • Increase efficiency
  • Optimize workforce allocation

Step 2: Use the Instructor Library

• Library: Instructor Library to get structured data (e.g., JSON) from large language models
• Installation: pip install instructor
• Advantage: Easy integration and validation using Pydantic data models

Step 3: Define Data Models

• Enumerations (Enums): Predefined categories for validation
  • Example: Order Issue, Incorrect Item Received
• Pydantic Models: Define expected data structure and validation rules
  • Example attributes:
    • category (enum)
    • urgency (enum)
    • sentiment (enum)
    • confidence_score (float, 0 to 1)
    • key_information (list of strings)
    • suggested_action (string)

Step 4: Integration

• Combine Data Models and LLM
• Patch OpenAI Client with Instructor: Ensure response matches Pydantic data model
• Validation and Error Handling: Handles incorrect data by retrying with corrected context
• Practical Example: A customer ordered a laptop, received a tablet; urgency high, sentiment angry
• Output:
  • Metadata extracted
  • Suggested actions
  • Suitable for customer care systems
• Benefits: Reduces manual sorting and routing, improves efficiency

Step 5: Optimize and Experiment

• Prompt Optimization: Define clear instructions, contexts, roles, and examples in prompts
• Model Selection: Experiment with different models (e.g., GPT-3.5 Turbo, GPT-4)
  • Smaller models for simple tasks to reduce costs and increase speed
• System Extension: Further automate responses for simpler queries based on classification
• Results: Efficient classification with high confidence and accuracy

Conclusion

• Building a robust classification system involves:
  • Clear objectives and business understanding
  • Utilizing libraries like Instructor for structured responses
  • Defining detailed and validated data models
  • Effective integration for error handling and retries
  • Continuous optimization and experimentation

Additional Resources

• Freelancing Help: Link to Dave Abar's company offering help for developers looking to freelance
• Further Learning: Video link on Data Lumina's complete process for building AI applications

Call to Action

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