How to Evaluate AI Models for Your Product: Complete Guide
Step 1: Define Your Requirements
Before evaluating models, know what you need:
- What tasks will the AI perform?
- What quality level is acceptable?
- What’s your latency requirement?
- What’s your budget?
Step 2: Create a Test Set
Gather 20-50 real examples from your use case. Include:
- Typical inputs
- Edge cases
- Failure modes you’ve seen
Step 3: Run Benchmark Tests
Test each model on your test set. Score on:
- Accuracy (does it get the right answer?)
- Quality (is the output usable?)
- Consistency (does it perform reliably?)
Step 4: Evaluate Key Metrics
Quality Metrics
- Task completion rate
- Error rate
- Human preference score
Performance Metrics
- Latency (time to first token, total time)
- Throughput (tokens/second)
Cost Metrics
- Cost per 1K requests
- Cost per 1K tokens
Step 5: Consider Operational Factors
- API reliability
- Rate limits
- Geographic availability
- Vendor lock-in
Our Recommended Process
- Start with 2-3 models that fit your criteria
- Run 100 requests with each
- Compare costs and quality
- Pick the winner
- Re-evaluate quarterly