Model specification accuracy

Ensure AI model configurations accurately reflect official specifications and avoid hardcoded assumptions. Model parameters, token limits, capabilities, and naming should match vendor documentation rather than using generic defaults.

copy reviewer prompt

Prompt

Reviewer Prompt
Deploy Agent

Ensure AI model configurations accurately reflect official specifications and avoid hardcoded assumptions. Model parameters, token limits, capabilities, and naming should match vendor documentation rather than using generic defaults.

Key practices:

  • Verify token counts include both input and output limits (e.g., tokens: maxInput + maxOutput)
  • Avoid hardcoded parameter defaults that don’t apply universally (like default: 0.8 for strength)
  • Use consistent naming conventions (e.g., “Imagen 4” with spaces, remove “Instruct” for base models)
  • Add “Preview” or “Beta” labels for experimental models
  • Remove deprecated models only when official end-of-life dates are announced
  • Cross-reference specifications with official API documentation

Example of proper model configuration:

{
  description: 'Gemini 2.5 Flash 是 Google 最先进的主力模型',
  displayName: 'Gemini 2.5 Flash', // Clear, consistent naming
  id: 'google/gemini-2.5-flash',
  contextWindowTokens: 1_048_576, // Verified against official docs
  maxOutput: 65_535, // Separate input/output limits
  // No hardcoded parameter defaults
}

This prevents user confusion, billing errors, and ensures reliable model behavior across different AI providers.

Source discussions