TRAIL
Evaluate an AI product
Look beyond demos to evidence, access, limits, and consequences.
Every AI product demos well. This trail is the checklist for looking past the demo — at what the system actually is, what evidence backs its claims, what it touches, and what happens when it's wrong.
By the end you'll be able to interrogate a tool the way a skeptical buyer should: separating model from marketing, tracing claims to sources, probing for bias and security holes, and weighing the licensing and consequences before you commit.
Begin trail →- 1OrientModel, product, system
First question about any product: what are you actually buying? The same underlying model can be wrapped into wildly different systems with different guarantees.
- 2OrientFluent is not true
A slick demo proves the output is fluent, not that it's correct. Keep the two apart while you evaluate.
Practice · Reality Lab: Repair a confident draft Practice · Consequence Room: The number nobody checked
- 3OrientTraining and use are different
What a model learned in training and what it's given at use time are different things — and it decides how much a vendor's "it knows your domain" claim is worth.
- 4JudgeMatch checking to risk
Decide how much evidence this purchase deserves based on what it will be trusted to do. A toy and a decision system are not evaluated the same way.
Practice · Reality Lab: Ground an answer in its sources Practice · Reality Lab: Match the check to the risk Practice · Reality Lab: Verify a message that might be fake Practice · Consequence Room: The number nobody checked Practice · Consequence Room: The snippet that shipped
- 5JudgeTrace a claim
Take one concrete claim the product makes and follow it to a source you can verify. Claims that can't be traced are marketing.
Practice · Reality Lab: Repair a confident draft Practice · Reality Lab: Break a big ask into steps Practice · Reality Lab: Ground an answer in its sources Practice · Reality Lab: Make the draft your own Practice · Reality Lab: Match the check to the risk Practice · Consequence Room: The number nobody checked
- 6JudgeBias and representation
Probe who the system works less well for. A product that performs unevenly across people is a liability, not a feature.
- 7JudgePrompt injection
If the tool reads outside content or calls tools, it can be hijacked by what it reads. Ask how the vendor separates instructions from untrusted input.
Practice · Consequence Room: The assistant that reads the web
- 8JudgeHuman oversight
Check whether the product actually leaves room for a human to understand and override it — or whether oversight is theater.
Practice · Reality Lab: Turn a process into an agent workflow Practice · Consequence Room: The assistant that reads the web Practice · Consequence Room: The snippet that shipped Practice · Consequence Room: The shortlist machine
- 9JudgeVendor evidence
Now ask the vendor for the thing that would actually change your mind: results on your own examples, and an honest account of what the system gets wrong.
- 10JudgeConflicting sources
Before you trust any summary the tool produces, check whether it flattened a real disagreement into one smooth answer. The disagreement was the information.
- 11JudgeReality Lab: evidence
A hands-on check: work through the evidence for a claim yourself, so evaluating stops being abstract.
- 12JudgeCopyright and permission
The last dimension buyers forget: what the tool is trained on and what you're licensed to do with its output. Rights and permission close the evaluation.
Practice · Reality Lab: Make the draft your own Practice · Consequence Room: The image you didn’t own