AI Evaluation: How to Know If It's Working

Traditional software is easy to test. Input A produces output B. Done. AI doesn't work that way. The same input can produce different outputs, and "correct" is often subjective.

So how do you know if your AI feature is actually working? You need an evaluation strategy.

The Evaluation Mindset

First, accept that AI evaluation is ongoing, not one-time. You're not testing for bugs; you're measuring quality over time. Quality drifts. User expectations change. Models update. What works today might not work next month.

Build evaluation into your workflow, not as an afterthought.

Define Success Criteria First

Before measuring anything, define what "good" means for your use case:

For classification tasks: What accuracy is acceptable? 90%? 95%? What's the cost of false positives vs. false negatives?

For generation tasks: What makes output "good"? Factual accuracy? Tone? Length? Actionability?

For search/retrieval: What's acceptable relevance? Should the right answer be in the top 1 result? Top 5?

Write these down. Without clear criteria, you'll chase your tail trying to optimize everything.

Build an Evaluation Dataset

You need labeled examples to measure against. This is tedious but essential.

Start with 50-100 examples: Cover your main use cases plus edge cases. Include examples you expect to work and examples you expect to fail.

Label them manually: For each input, write down the ideal output. For classification, that's the correct category. For generation, it might be key points that must be included.

Include edge cases: Empty inputs, very long inputs, ambiguous cases, adversarial inputs. You need to know how the system behaves at the margins.

Update regularly: When you find failures in production, add them to your eval set. This prevents regressions.

Quantitative Metrics

Different tasks need different metrics:

Classification

• Accuracy: Percentage of correct predictions
• Precision: Of items predicted as X, how many actually were X?
• Recall: Of actual X items, how many did we catch?
• F1 Score: Balance of precision and recall

Don't just look at overall accuracy. If 90% of your data is category A, a model that always guesses A has 90% accuracy but is useless.

Generation

Generation is harder to measure automatically. Some options:

• BLEU/ROUGE scores: Measure overlap with reference text. Useful but limited.
• Length conformance: Did output meet length requirements?
• Format compliance: Did it follow the requested structure?
• Factual extraction: Did it include required facts from the source?

Search/Retrieval

• Precision@K: Of top K results, how many were relevant?
• Recall@K: Of all relevant items, how many appeared in top K?
• MRR: Mean Reciprocal Rank, how high does the first relevant result appear?

Qualitative Evaluation

Numbers don't capture everything. You also need human evaluation.

Side-by-side comparison: Show evaluators two outputs (from different prompts, models, or versions) without labels. Ask which is better. This controls for bias.

Rating scales: Have evaluators rate outputs on specific dimensions: accuracy (1-5), helpfulness (1-5), tone (1-5). Average across evaluators.

Failure analysis: Don't just count failures. Understand them. Why did it fail? Was the input ambiguous? Did the model misunderstand? Is there a pattern?

Aim for at least 3 evaluators per example to reduce individual bias.

Automated Evaluation with LLMs

Here's a useful trick: use one LLM to evaluate another. Ask GPT-4 to rate outputs from GPT-3.5-turbo.

A simple eval prompt:

Rate the following response on a scale of 1-5 for accuracy, helpfulness, and relevance.

Question: [original question]

Response: [model output]

Ratings (JSON):

This scales better than human evaluation, though it has its own biases. Use it for rapid iteration, then validate with humans periodically.

Production Monitoring

Lab evaluations aren't enough. You need to monitor in production:

Implicit feedback: Track user behavior. Did they accept the AI output? Edit it heavily? Retry? These signals indicate quality.

Explicit feedback: Add thumbs up/down buttons. Users won't always click them, but when they do, that data is gold.

Latency tracking: Slow responses frustrate users even if they're accurate. Track P50, P95, and P99 latencies.

Cost per interaction: Monitor token usage. Unexpected spikes might indicate prompt issues or abuse.

Continuous Improvement Loop

Evaluation should drive improvement:

Weekly reviews: Look at your lowest-rated outputs. What's causing failures? Are there patterns?

Prompt iteration: Test new prompts against your eval set before deploying. Only ship changes that improve metrics.

Dataset expansion: When you find new failure modes, add them to your eval set. Your evaluation should get harder over time.

Version tracking: Log which prompt/model version generated each output. This lets you compare performance over time and roll back if needed.

Warning Signs

Watch for these red flags:

• Declining scores over time: Model or data drift happening
• High variance: Quality is inconsistent, prompt needs work
• Specific failure clusters: Certain input types consistently fail
• User feedback spikes: Sudden increase in negative feedback

Don't wait for users to complain. Catch problems in your metrics first.

The Minimum Viable Evaluation

If resources are tight, here's the bare minimum:

• 50 labeled test examples
• Weekly automated eval runs
• Thumbs up/down in production
• Monthly review of worst outputs

That's enough to catch major problems and drive gradual improvement. You can sophisticate from there as the feature grows.

AI without evaluation is just guessing. Measure, iterate, improve.