Understanding Contracts for LLM Libraries

Introduction

As Large Language Models (LLMs) become increasingly integrated into software systems, understanding how to properly interact with LLM libraries becomes crucial. This post explores my ongoing research into contracts and patterns for LLM library usage.

The Challenge

LLM libraries present unique challenges:

• Complex input/output relationships
• Probabilistic behavior
• Resource management concerns
• Version compatibility issues
• Error handling complexities

Common Patterns

Through analysis of Stack Overflow posts and GitHub issues, we’ve identified several patterns:

Input Validation

def validate_prompt(prompt: str) -> bool:
    # Length checks
    if len(prompt) > MAX_LENGTH:
        raise ValueError("Prompt exceeds maximum length")
    
    # Content validation
    if contains_sensitive_data(prompt):
        raise SecurityError("Prompt contains sensitive data")
    
    return True

Error Handling

• Token limit exceeded
• API rate limiting
• Model availability
• Response validation

Best Practices

1. Always validate inputs
2. Implement proper error handling
3. Monitor token usage
4. Cache responses when appropriate
5. Implement retry mechanisms

Future Work

• Developing formal verification methods
• Creating testing frameworks
• Building automated validation tools

References

• LLM API Design Patterns
• Contract Programming for ML

Discussion

What patterns have you observed in LLM library usage? Share your experiences in the comments!