Our article standard

A guide should help you make a real choice about an AI system. It should explain what to test, show how the results lead to a decision, and be clear about what the test cannot prove.

Start with your decision

Each guide names the person it is for and the job they need to do. A team choosing live captions needs to know about delay and missed names. A team processing invoices needs to know which wrong fields could reach payment. We choose the measures that matter to that job.

Show where the limit comes from

“Good enough” needs a reason. A delay limit might come from how long a user can wait. A review limit might come from how many cases a team can check each day. An alert limit might come from the number of interruptions an operator can handle. Our example limits teach the method; they are not standards for every product.

Make the example do the work

We show the test inputs, the count or calculation, and the choice that follows. If 600 ordinary audio clips arrive each hour and a system wrongly flags 1% of them, that means about six false alerts an hour. If the team can handle only two, that setting fails even if the model looks accurate overall. See the audio alert guide for a full threshold comparison.

Use visuals that answer a question

A table or diagram should help you compare options, find a failure, or see where a decision changes. We give it a clear caption and explain the point in the surrounding text. A blank matrix or a row of process boxes does not help you choose.

Make the test repeatable

We explain what goes into the test set, how examples are labeled, and which settings must stay the same across candidates. The questions or recordings used to tune a system should stay separate from those used for its final check. Each evaluation guide offers an item-level results sheet so you can keep the errors behind the average.

Say what the evidence cannot show

Worked numbers are labeled as examples. We do not present them as tests run by BuildAI. A small test, or one with no observed errors, does not prove a system will work everywhere. When a result is near a pass line, we say what more should be tested before relying on it. A different workload, device, language, or policy version may change the answer.

Let readers check the sources

We link to primary research or documentation near the claims they support. A source that defines a metric does not prove our suggested product limit is safe. Older BuildAI comparisons remain available as history, but we do not use their numbers as current evidence unless their underlying tests can be checked.

Before we publish

An editor checks the example arithmetic, source links, diagrams, plain language, and whether the guide lets its intended reader reach a defensible choice. We check the built page and its links. After publication, we review reader feedback and update a guide when the method, source, or product setting changes.

When a guide is wrong or hard to follow, we should correct it rather than leave a confident but unhelpful answer online.