How to Choose the Right AI Model for Your Application

Short summary

A practical framework for choosing AI models based on task requirements rather than benchmarks or brand recognition. Model selection is a multi-objective trade-off involving output quality, latency, cost, context length, and structured output stability. The article recommends building evaluation sets with edge cases, defining task-specific criteria, and tracking failure rates on critical business scenarios rather than relying on average scores.

• •Model selection should start with business tasks, not brand names—different tasks need different models
• •Build evaluation sets covering normal, ambiguous, oversized, multilingual, and failure-prone inputs
• •Track failure rates on critical business scenarios separately from average scores to avoid hidden production risks

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