Your AI demo works. Now prove that it's reliable.

Every AI product starts with a demo that works.

The model responds well. The output looks good. The team is impressed. Someone says "we should ship this."

This is the most dangerous moment in an AI product's life.

The gap between demo and production

A demo is optimised. The inputs are chosen to show the model at its best. The use cases are clear, the queries are clean, and someone with context is watching the output.

Production is different. Users ask questions you didn't anticipate. They paste in malformed data. They ask about edge cases. They use the product at 2am when they're tired and their query is half a thought.

The model that performed beautifully in the demo will encounter all of this. The question is whether you've decided what "good enough" means before or after you find out.

Building test cases from real user questions

The most reliable source for evaluation is real usage data. If you don't have it yet, the next best thing is structured assumption.

What are the 20 most likely things a user will actually ask? What are the 5 most likely misuses? What happens when the input is incomplete, ambiguous, or in the wrong format?

Write these down as test cases before you ship. Define what a good response looks like for each one. This is not glamorous work. It's also not optional if you care about reliability.

Hallucinations vs. silent failures

There are two types of AI failures that matter differently.

Hallucinations are visible. The model confidently states something false. Users often catch these — they're wrong in an obvious way.

Silent failures are harder. The model gives a response that sounds correct, is formatted correctly, passes a surface-level review, and is subtly wrong in a way that only someone with deep domain knowledge would catch.

Silent failures are the ones that erode trust over time. Users don't know why the product feels unreliable. They just stop using it.

Your evaluation framework needs to catch both — which means you need evaluators who know the domain, not just people checking whether the output "looks good."

Defining confidence and escalation

Not every query should be answered. Some queries are outside the model's reliable scope. Some involve information the model doesn't have. Some require a level of accuracy the system can't guarantee.

A reliable AI product knows this about itself.

Practically: define categories where the system should express uncertainty. Build in language that signals low confidence. Create escalation paths — to a human, to a different source, to an honest "I don't know."

"I don't know" is a feature, not a failure.

Regression testing after changes

AI systems degrade silently. A prompt change that improves performance on one case can quietly break five others. A model update from the provider changes behaviour in ways you didn't test for.

Treat your test cases as a regression suite. Run them after every significant change. Compare outputs not just for correctness but for consistency — if the answer to the same question changes significantly between runs, that's a signal worth investigating.

Human evaluation vs. automated evaluation

Automated metrics — BLEU scores, similarity measures, LLM-as-judge — are useful for scale. They can tell you when something has clearly gone wrong, quickly, across many cases.

They can't tell you whether the output is actually useful to a real human in a real context.

Build both. Use automated evaluation to catch regressions. Use human evaluation, regularly, to catch the things automated systems miss.

The most important evaluator is a domain expert who will tell you honestly when the output isn't good enough — not a stakeholder who wants the project to succeed.

The demo is not the product. The demo is a hypothesis.

Shipping is when you find out whether the hypothesis is true. Evaluation is how you find out faster, with less damage, before your users find out for you.