TL;DR: Evaluate AI models on five criteria that actually matter: task-specific accuracy, consistency, cost at your scale, integration complexity, and vendor reliability. Ignore benchmark leaderboards. Test with your actual data and actual use cases.
Every company picking an AI model makes the same mistake: they read benchmark comparisons, watch demos, and pick the one that "seems best." Then they spend three months building on it and discover it doesn't work for their specific use case.
Here's how to evaluate properly.
## Step 1: Define Your Criteria
AI companies publish benchmarks making their models look good. That's marketing.
What's the primary task? Be specific. Not "customer service" — "Answering technical questions about our SaaS product's API using our documentation as context."
What does good look like? Define success before testing. Accuracy rate, response time, tone.
What does bad look like? What failures are unacceptable? Hallucinated answers? Contradicting documentation? Know your dealbreakers.
## Step 2: Build a Test Dataset
Use real data: 50-100 real customer questions, correct answers, edge cases that tripped up your human team, and questions that should trigger escalation.
## Step 3: Test for Accuracy
Run the test set, score each response. Pay attention to error types. Confident wrong answers are worse than uncertain ones. Pattern errors matter more than random ones.
## Step 4: Test for Consistency
Run the test set three times. Compare outputs. A good model gives substantially similar answers every time. Also test with rephrased questions — five ways of asking the same thing should produce consistent answers.
## Step 5: Test at Your Scale
Calculate expected monthly volume:
- Input/output tokens per request - Requests per day/month - Cost per 1M tokens per model - Total monthly cost at full scale
Make sure unit economics work at production volume, not demo volume.
## Step 6: Evaluate Integration
- API quality: Documentation, SDKs, rate limiting - Latency: Over 3-5 seconds feels slow for customer-facing - Uptime: Check status pages and community reports - Context window: Can it handle your document sizes?
## Step 7: Assess Vendor Risk
- Financial stability: Will they exist in two years? - Pricing history: Sudden raises? - Data policies: Used for training? DPA/BAA available? - Lock-in risk: How hard to switch? Build for portability.
## The Scorecard
| Criterion | Weight | |-----------|--------| | Task accuracy | 30% | | Consistency | 25% | | Cost at scale | 20% | | Integration ease | 15% | | Vendor reliability | 10% |
Score each model 1-5, apply weights. Math tells you which to pick.
## Frequently Asked Questions
How many models to evaluate? Three is enough. Pick top candidates, run full evaluation. Don't test everything.
How long should evaluation take? One to two weeks. Don't rush — bad choice costs months in rework.
Should I evaluate open-source models? If you have engineering resources for deployment and maintenance. Otherwise stick with commercial APIs.
What if two models score similarly? Better vendor support and pricing trajectory breaks the tie.
Should I hire a consultant? If AI selection isn't your core competency and the decision significantly impacts your business, yes. Expert evaluation cost is trivial compared to picking wrong.
How often to re-evaluate? Every 6-12 months, or on major model launches.
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