This website and its content are generated by AI. This website is created for demonstration purposes.

Model Selection Strategy

Model Selection Strategy #

Choosing the right foundation model for your application is a critical decision that impacts cost, performance, capabilities, and operational requirements.

Pattern Description #

This pattern provides a structured approach to selecting appropriate foundation models based on your specific requirements and constraints.

Key Considerations #

1. Capability Requirements #

  • Task Domain: What tasks must the model perform? (text generation, code generation, image creation, etc.)
  • Quality Bar: What level of output quality is acceptable?
  • Specialized Knowledge: Does the model need domain-specific knowledge?
  • Reasoning Ability: How complex is the reasoning required?
  • Instruction Following: How precise must the model follow instructions?

2. Operational Constraints #

  • Latency Requirements: What response time is acceptable?
  • Throughput Needs: How many requests per minute must the system handle?
  • Cost Sensitivity: What is the budget for model inference?
  • Data Privacy: Can data be sent to external APIs or must processing be done on-premise?
  • Deployment Environment: What compute resources are available?

3. Implementation Options #

Cloud API-based Models #

  • Examples: OpenAI GPT-4, Anthropic Claude, Google Gemini
  • Advantages: No infrastructure to manage, continuously improved, easy to integrate
  • Disadvantages: Higher per-token costs, potential vendor lock-in, data leaves your environment

Self-hosted Models #

  • Examples: Llama 2, Mistral, Falcon
  • Advantages: Fixed costs, data privacy, customization control
  • Disadvantages: Requires infrastructure management, potentially lower capabilities

Decision Framework #

  1. Start with capability requirements to create a shortlist
  2. Filter by operational constraints
  3. Consider hybrid approaches (multiple models for different functions)
  4. Evaluate top candidates with representative test cases
  5. Calculate total cost of ownership for final candidates
  6. Make selection with consideration for future flexibility

Implementation Example #

# Example model router that selects between models based on task complexity
def select_model(task_description, input_length, importance):
    if importance == "critical" and input_length > 1000:
        return "gpt-4"
    elif complexity_score(task_description) > 0.7:
        return "claude-2"
    else:
        return "mistral-7b-instruct"