A model scores well on public benchmarks. You deploy it. It performs poorly on your actual use case.

This happens constantly. Standard benchmarks measure general capability, not performance on your specific tasks. Building evaluation systems that measure what actually matters is essential but underinvested work.

Why Standard Benchmarks Fail

Public benchmarks have important limitations:

Distribution mismatch. Benchmarks use specific data distributions. Your data distribution is different. Performance doesn’t transfer.

Task mismatch. Benchmark tasks may not resemble your actual tasks. Summarization performance doesn’t predict performance on your specific summarization needs.

Gaming. Models are increasingly optimized for benchmark performance. This can come at the expense of real-world performance.

Staleness. Benchmarks can become contaminated as test examples leak into training data. Old benchmarks may not measure current model capability fairly.

Missing dimensions. Benchmarks measure specific capabilities. Your use case may depend on capabilities not measured.

The implication: you need your own evaluation systems.

What Good Evaluation Requires

Effective evaluation systems have several components:

Representative test sets. Examples that represent your actual use cases. Not general examples - your specific examples.

Clear success criteria. How do you know if an output is good? This needs to be defined precisely enough to measure.

Consistent measurement. Evaluation that produces stable, reproducible results. Automated where possible.

Coverage of important cases. Both typical cases and edge cases that matter. The edge cases are often where evaluation is most valuable.

Tracking over time. Performance changes as models update, data drifts, and use cases evolve. Continuous measurement matters.

Building Test Sets

The test set is the foundation. Getting it right is crucial:

Source from real data. Take examples from actual use. Random sampling from production data gives representative distribution.

Include known-hard cases. Deliberately include cases that are expected to be difficult. These reveal model limitations.

Label carefully. Human labeling should be consistent and accurate. Multiple labelers with adjudication for disagreements.

Size appropriately. Large enough for statistical significance on metrics you care about. Smaller is fine for qualitative assessment.

Keep it secret. If models are fine-tuned or selected based on test set performance, the test set becomes training data. Hold out evaluation data carefully.

Defining Success Metrics

Metrics need to match what you actually care about:

For classification: Accuracy, precision, recall, F1 - but weighted for the errors that matter most. A false positive and false negative may have very different costs.

For generation: Harder to measure automatically. Consider factual accuracy, relevance, style matching, harmful content avoidance.

For retrieval: Precision and recall at relevant cutoffs. Mean reciprocal rank. Normalized discounted cumulative gain.

Business metrics: Ultimate success is business impact. Link AI metrics to business outcomes where possible.

Resist single-number metrics that hide important variation. A model with 90% accuracy that fails on all high-value cases is worse than 85% accuracy distributed evenly.

Automated vs. Human Evaluation

Both have roles:

Automated evaluation is scalable, consistent, and fast. Use it for properties that can be measured programmatically - format compliance, factual accuracy against databases, classification agreement.

Human evaluation captures judgment that’s hard to automate. Use it for quality assessment, preference comparison, and nuanced judgments.

LLM-as-judge is a middle ground - using AI to evaluate AI outputs. Faster than humans, more nuanced than simple automation. But has its own biases and limitations.

Most mature evaluation systems combine all three.

Evaluation Infrastructure

Building evaluation into development workflows:

Continuous evaluation. Run evaluation on every model change. Catch regressions immediately.

Dashboards. Visible metrics that teams can monitor. Performance trends over time.

Alerting. Automated alerts when performance degrades beyond thresholds.

Versioning. Track which model version produced which results. Enable rollback if needed.

Reproducibility. Evaluation runs should be reproducible. Same inputs produce same outputs.

This is engineering work that requires investment. But without it, you’re operating blind.

Common Mistakes

What goes wrong with evaluation:

Too little investment. Building evaluation is treated as overhead, not core work. This leads to inadequate evaluation that misses important problems.

Optimizing for the wrong thing. Improving benchmark scores without improving real-world performance.

Evaluating once. Doing evaluation at deployment, then never again. Performance drifts; continuous evaluation is necessary.

Ignoring qualitative review. Numbers are important but don’t capture everything. Humans looking at outputs catches issues metrics miss.

Leaking test data. Test examples end up in training through various paths. Vigilance is required.

Getting Started

For organizations building evaluation capability:

Start with real examples. Even a small set of labeled real examples is better than no evaluation.

Define critical failures. What errors are unacceptable? Ensure evaluation detects these specifically.

Automate what you can. Basic automated checks catch obvious problems and run continuously.

Build review processes. Regular human review of model outputs supplements automated metrics.

Iterate on evaluation. Evaluation systems improve with experience. Expand and refine over time.

Working with AI consultants Brisbane can help establish evaluation practices, especially for organizations new to production AI systems.

The Investment Case

Evaluation feels like overhead. It’s actually investment.

Without proper evaluation:

• You don’t know if changes improve or degrade performance
• Regressions ship to production
• Problems are discovered by users, not testing
• Model selection is guesswork

With proper evaluation:

• Changes are validated before deployment
• Regressions are caught before users see them
• Model selection is data-driven
• Improvement is systematic rather than random

Organizations like Team400 are increasingly helping enterprises build evaluation capability alongside AI implementations, recognizing that sustainable AI requires knowing whether it works.

The models get all the attention. The evaluation systems determine whether the models create value. Build both.