How to Evaluate AI Models for Your Product
Key Takeaways
- •Benchmarks measure academic performance not product performance
- •Build 50-100 representative test examples before evaluating any models
- •Run blind evaluations across 3-4 models to avoid brand bias
- •The second-best model is often 5x cheaper and only 3-5% worse
Benchmarks Lie
Every model provider publishes benchmarks showing their model is the best. The truth: benchmarks measure academic performance, not product performance. A model that scores highest on MMLU might be terrible at your specific use case.
Our Evaluation Framework
When a client asks us to integrate AI, we run a four-step evaluation before writing any production code:
Step 1: Define Success Criteria
What does "good enough" look like for your specific use case? For a customer support chatbot, maybe 85% of responses need zero human edits. For medical document summarization, maybe 99% accuracy on key findings is the minimum. Define this before testing any models.
Step 2: Build a Test Dataset
Create 50-100 representative examples of real inputs your system will handle. Include edge cases, ambiguous inputs, and adversarial examples. This dataset becomes your ground truth for comparing models.
Step 3: Run Blind Evaluations
Test 3-4 models against your dataset. Score outputs on accuracy, relevance, format compliance, and latency. Use both automated scoring (for format and keyword matching) and human evaluation (for quality and nuance). Remove model names to avoid bias.
Step 4: Cost-Quality Analysis
Plot model quality against cost per request. Often the second-best model is 5x cheaper and only 3-5% worse. For most products, that is the right trade-off.
Models We Test in 2025
For text generation: GPT-5, GPT-5-mini, Claude 3.5 Sonnet, Gemini 2.5 Pro, Gemini 2.5 Flash. For embeddings: OpenAI text-embedding-3-large, Cohere embed-v3. For code: Claude 3.5 Sonnet, GPT-5. Test at least three before committing.
Frequently Asked Questions
How do you choose the right AI model?
Use a 4-step framework: define success criteria for your use case, build a test dataset of 50-100 real examples, run blind evaluations across 3-4 models, and analyze cost-quality trade-offs.
Are AI benchmarks reliable for choosing models?
No. Benchmarks measure academic performance, not product performance. A model scoring highest on MMLU might perform poorly for your specific use case.
How many AI models should you test?
Test at least 3-4 models against your specific use case before committing. Include both premium and budget options to understand the cost-quality trade-off.