Evaluating Models for Your Product

Series: Claude Learning Journey · Expert

Most developers evaluate models the way they evaluate programming languages: they read benchmarks and form opinions without testing on their actual workload. That is an unreliable method. The model that wins the benchmarks is not necessarily the model that works best for your specific problem.

This post is about how to evaluate models systematically for your use case.

What You Are Actually Measuring

Model evaluation is not measuring intelligence. It is measuring fitness for purpose. The questions are practical: does this model produce outputs that are good enough for our users? Does it do so reliably? Does it do so at a cost and latency we can sustain?

The benchmarks that matter for your product are the ones that reflect your actual usage, not the standard academic benchmarks that model providers publish.

Building an Evaluation Dataset

You need data to evaluate models. Specifically, you need:

• Inputs that represent what your product actually receives
• Outputs that represent what good looks like
• A way to compare model outputs against that standard

Creating this dataset is work. It is worth doing because it transforms your model evaluation from impressionistic to systematic.

The practical approach: collect a sample of 50-100 real inputs from production. Have someone with domain expertise rate them for quality. Use that as your benchmark.

What to Measure

The useful measurements for product model evaluation:

Quality: is the output good enough to ship to users? Rate on a simple scale: unacceptable, acceptable, good.

Consistency: does the model produce the same quality on repeated runs with the same input? Variance matters as much as average quality.

Latency: how long does it take to produce an output at your required quality level? There is often a quality-latency tradeoff.

Cost: what does each output cost at your expected volume? Cost per useful output, not cost per token.

What You’ll Learn

• Why standard benchmarks rarely reflect product needs
• How to build an evaluation dataset from production data
• The four metrics that matter: quality, consistency, latency, cost
• Making the quality-latency-cost tradeoff explicit

Try It Yourself

Take your last 20 Claude outputs for your product. Rate each one: would you ship this? Now look at the pattern. Where is Claude consistently good? Where does it consistently fail? That pattern is more useful than any benchmark for deciding how to use Claude in your product.

What’s Next

Evaluation tells you whether Claude is working for your product. The next question is whether you should build a product with Claude at its core — what it takes to monetise AI effectively.

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Part of the Claude Learning Journey series · Next: Monetisation: Building Products That Use Claude