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We help developers, technical product people, and buying teams understand how AI tools work beneath the chat window, and judge them on evidence. Mechanism first, product second, vendor questions last.

How to Evaluate an AI Tool in Thirty Minutes Your trial expires on Friday. You have signed up, poked at it twice, and formed no real opinion. This happens constantly, and it is why organisations end up buying tools on the strength of a demo rather than a test.
Thirty minutes of structured evaluation will tell you more than three weeks of casual use. Here is how to spend them.
Minutes 0 to 5: bring your own work Do not use the sample data. Do not use the suggested prompts. Both were chosen because the tool handles them well.
Pick three real tasks from your own week. Not hypotheticals. Actual things you or your team did recently, where you still have the input and you know what a good output looks like because you produced one.
Choose them for variety:
One routine task you do constantly. This is where cumulative value lives. One task that is genuinely hard, where you would expect a person to struggle. One task the tool should refuse or flag, like something needing information it cannot have.
That third one is the most revealing and almost nobody includes it.
Minutes 5 to 15: run all three Give each task a proper prompt. Context, the task stated clearly, the format you want. You are evaluating the tool, not your ability to write terse instructions.
While the outputs come back, watch for four things.
Does it ask? When you leave something genuinely ambiguous, does the tool request clarification or does it guess silently? Guessing silently is a meaningful negative, especially for anything that touches your data.
Does it admit limits? On the task requiring information it cannot have, does it say so, or does it produce something confident and wrong? This single test separates tools that have been carefully built from tools that are a thin wrapper over a model.
How is it with your format? If you asked for a table with specific columns, did you get exactly that, or something adjacent that you now have to reformat?
How fast, really? Not the marketing number. How long did your actual three tasks take, including the rounds of correction.
Minutes 15 to 25: check the work properly This is the part people skip, and it is where evaluations either become useful or become theatre.
Take the output from the hard task and verify it line by line. Every specific: names, figures, dates, references, quoted material. Check them against the source you supplied or against a primary source.
You are looking for one thing above all: does this tool invent things when it does not know?
Every language model will do this to some degree. That is the underlying mechanism. But tools differ enormously in how much work has gone into reducing it. Some ground their answers in documents you supply and cite the passage. Some quietly fill gaps. The difference will not show up in a demo, and it is the single biggest determinant of whether the tool is safe for real work.
Then look at the output from the routine task and ask a harder question: is this actually better than what we produce now, or just faster? Faster and slightly worse is a real trade, and sometimes worth it. But you should make that trade knowingly.
Minutes 25 to 30: the questions the product page will not answer Five questions. Find the answers in the documentation, or ask sales and get it in writing.
Is our data used for training? Assume yes unless you have confirmed otherwise in writing. Defaults differ between free and paid tiers of the same product, which catches people out.
Where is the data stored, and how long is it retained? "Deleted when you delete the conversation" and "retained for thirty days for abuse monitoring" are both common and very different.
What happens on the way out? Can you export your prompts, your outputs, your configurations? A tool that becomes central to a workflow and cannot be exited is a liability you will discover at renewal.
Who is responsible when the output is wrong? You already know the answer is you. Ask anyway, because how they answer tells you how the company thinks about the problem.
What does this cost at real volume? Price per seat is rarely the whole picture. Find the usage limits, the overage rates, and what happens when a team member has an unusually heavy month.
Scoring it You now have enough for a decision. Four questions, answered honestly:
Did it do the routine task well enough to use without heavy editing? If not, the cumulative value case collapses, whatever else it does.
Did it handle the hard task better than a mediocre first attempt by a person? That is the bar. Not perfection.
Did it invent anything? If yes, how much work would catching that reliably take?
Can you live with the data terms?
A tool that does the routine task reliably and stays honest about what it does not know is worth more than one that is impressive on the hard task and occasionally makes things up.
What this test deliberately ignores Benchmark scores. They measure performance on standardised tasks that are not your tasks, and the gap between benchmark performance and usefulness on your work is wide enough to make them close to useless for this decision.
Feature lists. Most features in an AI product go unused. The one or two you will actually touch daily are what matter.
The demo. Demos are constructed from cases the tool handles well. That is not dishonest, it is what a demo is for. It just tells you nothing about your own work.
The mistake that costs the most Evaluating with your best prompts rather than realistic ones.
You are careful and specific. Your colleagues will type six words and hit enter. If the tool only performs well under expert prompting, it will disappoint across a team, and the rollout will be blamed on the people rather than the fit.
Have someone who has not read the manual run the same three tasks. If their results are much worse than yours, you have learned something important about what adoption will actually require.

Everything AI - beyond the hype

Tokens, and Why Your Bill Is Shaped Like That
The first surprising AI invoice usually arrives in month two, after someone has started pasting whole documents into prompts.
Understanding why takes one concept, and it explains most of the pricing behaviour you will run into.
What a token is Models do not read words. They read tokens, which are chunks of text averaging roughly three quarters of a word in English.
Common words are usually a single token. Longer or unusual words split into several. Punctuation and spaces count. Code and non-English text tend to use more tokens per unit of meaning, which is why the same content costs more in some languages than in others.
A rough conversion that is good enough for planning: 1,000 tokens is around 750 words. A page of dense text is somewhere near 500 tokens. A 40-page report is perhaps 20,000.
Why the bill is shaped the way it is You are charged for tokens in and tokens out, usually at different rates, with output typically costing more.
That has a consequence worth internalising: the length of what you send matters as much as the length of what you get back.
Ask a one-line question and get three paragraphs, and you have used a few hundred tokens. Paste a 40-page report and ask for a one-line summary, and you have used twenty thousand, almost all of it on input, for an answer of twelve words.
This is why the expensive workflows are almost always document-heavy ones. Contract review, research synthesis, anything that pushes large source material through the model repeatedly.
The bit that catches teams out In a continuing conversation, the entire history is re-sent with every message.
Turn one costs you your prompt plus the answer. Turn two costs you the prompt, the answer, your new message, and the new answer. Turn ten includes everything that came before it.
Costs in a long conversation do not grow linearly. They accelerate. A forty-exchange session with a large document attached can cost many times what the same forty exchanges would cost spread across fresh conversations.
There is a practical habit here that saves money and improves quality at the same time: start a new conversation for each distinct task. You get a cleaner context and a smaller bill.
Tokens as a ceiling The second place you meet tokens is the context window, which is the total the model can hold in view at once.
That budget covers everything: your instructions, any documents you attached, the conversation so far, and the response being generated. When it fills, the oldest material falls out of view. The model does not announce this. It simply stops being able to see the beginning of your conversation, and answers as though it was never there.
If a long session starts producing answers that contradict something established early on, this is usually why.
What to do with this Send what is needed. Extract the relevant three pages rather than attaching the whole report. Quality improves too, since the model is not hunting through material that does not bear on the question.
Start fresh per task. Cheaper, and a cleaner context produces better answers.
Check where your spend actually goes. Most teams assume it is spread across everyone. It is usually one workflow, run by two people, pushing large documents through repeatedly. Finding it is often the whole optimisation.
Do not over-engineer this. For most individual professional use, token costs are small enough not to think about. This matters at team scale, in document-heavy workflows, and when you are building something on an API. For everyday use, write the prompt you need and stop worrying.
Why prices keep falling Worth knowing for planning. The cost of running a model, as opposed to training one, has been on a sustained downward path, driven by hardware improvements, better serving techniques, and smaller models that match the older large ones on many tasks.
The planning consequence: a workflow that does not quite justify its cost today may well justify it within a year or two, without you changing anything. Worth revisiting the ones you rejected on price.

When a Spreadsheet Beats an AI Tool
If a task has one correct answer produced by a fixed rule, use something that follows rules.
Arithmetic is the clearest case. A language model predicts plausible text, and the text of a correct sum looks very much like the text of an incorrect one. Ask it to add a column of forty figures and you will usually get the right answer, which is worse than always getting it wrong, because you stop checking.
Many tools now handle this by writing and running actual code when they detect a calculation. That genuinely fixes the problem, but you need to know whether yours does it, and whether it did it on this particular request. If you cannot see the calculation, do not trust the number.
The broader category Arithmetic is one instance of a general rule. Reach for deterministic tools when:
The answer is computed, not composed. Totals, conversions, date arithmetic, percentages.
The rule is fixed and known. Tax bands, shipping tiers, commission structures, eligibility criteria. A formula does this correctly every time and shows its working.
You need the same input to give the same output. Reconciliation, compliance checks, anything auditable. Built-in variation is a feature in writing and a defect here.
The data is structured and large. Ten thousand rows belong in a database or a spreadsheet, not a prompt.
Where the line actually sits The useful split is not "numbers bad, words good." It is closer to this:
Computing a figure is a job for a spreadsheet. Explaining what the figure means, writing the commentary, spotting which variances deserve attention: those are good AI tasks, working from numbers you calculated properly.
That combination is where most finance teams land, and it works well. The spreadsheet produces the truth. The model produces the narrative around it, from figures you supplied rather than figures it generated.
The test Before opening an AI tool, ask whether the task has a single right answer that a rule would produce.
If yes, use the rule. You will get the correct answer, the same answer every time, and a record of how it was derived.
If the answer depends on judgment, phrasing, or synthesis, you are in the right place.
Using a language model for arithmetic is not a small inefficiency. It is choosing a probabilistic tool for a deterministic problem, and the errors it produces are the kind that look completely normal.

Six Ways to Fact-Check AI Output
The worst version of this is not catching an obvious error. It is a colleague catching a plausible one, in a meeting, in front of people, in a document with your name at the top.
Checking AI output is not optional diligence you do when there is time. It is a required step in the process, for the same reason proofreading is required: the thing you are producing carries your name whatever produced the first draft.
Six practices cover most of it.
1. Treat every specific as unconfirmed Names. Numbers. Dates. Citations. Quotations. Percentages. Statute references. URLs. Job titles. Company names.
Every one of those is a specific, and every specific is a claim the model generated rather than retrieved. Some will be right. You cannot tell which by reading.
What holds up well, in practice, is the general reasoning and the structure. A model asked to analyse a situation will usually produce a sensible framework with the right considerations in roughly the right order. It is the details hanging off that framework that break, and the details are what get quoted.
Useful discipline: read the output once for sense, then read it again with a highlighter, marking every specific. That second read produces your checking list.
2. Check against a primary source Not another AI. Not a summary. The actual thing.
If the output cites a study, find the study. If it quotes a policy, open the policy. If it gives a figure from a report, locate the figure in the report.
This sounds obvious and is routinely skipped, usually via a shortcut that feels reasonable: searching for the claim and finding something that seems to confirm it. That is weaker than it looks, because a search for a plausible-sounding claim often surfaces adjacent material that does not actually say the thing.
3. Never ask the model to check itself "Are you sure?" and "please verify this" do not do what people think.
The model has no separate faculty for checking. Asked to verify, it generates text that fits the pattern of verification, which means it will often produce a confident confirmation of something it invented two messages ago. It may also flip and apologise for a claim that was correct, because apologising is what follows a challenge in most of its training material.
The response to "are you sure" tells you nothing about accuracy. It tells you how the model continues text after that question.
A related trap: asking for sources after the fact. The model will produce sources, and they may be invented, because a plausible citation is exactly the kind of thing the mechanism is good at producing.
4. Confirm the source exists before confirming what it says Two steps, and people collapse them into one.
First: does this source exist at all? Is there a paper with that title by that author in that journal in that year? Fabricated citations are common, and they are convincing because they are assembled from real components. Real journal, plausible author name, sensible title, appropriate year.
Second, only once the source is confirmed real: does it actually say what the output claims? A real source misrepresented is more dangerous than an invented one, because it survives a casual check. Someone verifies the paper exists, stops there, and the misattributed claim goes through.
5. Scrutinise hardest where you know least This is the counterintuitive one, and it runs against what everybody actually does.
Natural behaviour is to read carefully in your area of expertise, where you are engaged and opinionated, and skim the unfamiliar parts because you have no basis for judgment.
That is exactly backwards. In your own field you will catch a mistake without trying. Outside it you have no defences at all, and the output reads just as fluently.
Practically: when AI output covers ground you do not know well, that section needs a primary source check or an expert read. It does not get a pass because you cannot evaluate it. That is the reason it needs one.
6. If being wrong would embarrass you, verify it The rule that covers everything the others miss.
Not every AI output needs forensic checking. A draft email to a colleague suggesting three meeting times does not require source verification. Proportion matters, and treating everything as high stakes means you will soon treat nothing as high stakes.
The test is simple. Imagine this specific claim turning out to be false, in front of the people who will read it. If that thought produces a flinch, check it.
That covers anything going to a client, anything a decision rests on, anything in a regulated context, anything that will be quoted, and anything with a number in it that someone might act on.
What checking is not It is not reading the output and finding it reasonable.
Reasonableness is guaranteed. The system is built to produce text that reads well, which means an error and a fact arrive in identical packaging. A fabricated statistic sits in a sentence looking exactly like a real one, in the same register, with the same confidence.
Your instinct that something "seems fine" is calibrated on human writing, where fluency does correlate with care. Here it does not, and the instinct will mislead you every time.
Making it stick The habit that works is mechanical rather than attitudinal.
Do not try to be more careful in general. Instead, build one concrete step into the process: before anything leaves your hands, highlight every specific and confirm each one against a source.
It takes less time than people fear, usually a few minutes, because most documents contain fewer hard specifics than they appear to. And it converts a vague intention to be careful into something you either did or did not do.

What "AI Hallucination" Actually Is
The word is unhelpful. It suggests a malfunction, something going wrong in a system that otherwise reports facts.
What is actually happening is the system working exactly as designed, in a situation where its design and your expectations diverge.
The mechanism, briefly These models generate text by predicting what comes next, drawing on patterns learned from an enormous quantity of writing. They are not looking anything up. There is no database being queried and no retrieval step.
So when you ask for the title of a paper on a topic, the model produces text in the shape of a paper title on that topic. Sometimes the pattern-matching lands on a real paper, because real papers were in the training material and their titles left traces. Sometimes it produces something that has every property of a real title except existing.
From inside the mechanism these two cases are identical. There is no internal signal distinguishing "I am reproducing something I encountered" from "I am constructing something plausible." Both are the same operation.
That is why hallucination cannot be patched out. It is not a bug sitting next to the useful behaviour. It is the same behaviour.
Why it always sounds certain The second half of the problem, and the more dangerous one.
Confidence in writing is a stylistic pattern, and the model reproduces it because most of what it learned from is written confidently. Textbooks, articles, documentation and reference material do not hedge every sentence.
There is no mechanism connecting tone to reliability. A fabricated figure and a verified one are delivered in the same voice, with the same steadiness. The model does not have a reliable internal sense of uncertainty that surfaces to you as hesitation.
This matters because humans read confidence as a signal. Someone speaking clearly and without hedging on a topic usually does know it. That heuristic is well-founded for people and completely broken here.
What it looks like in practice The recognisable patterns:
Citations that do not exist. Real journal, plausible author, sensible title, right year. Assembled from real components into something that was never published.
Numbers with no source. A market size, an adoption rate, a percentage improvement. Presented flatly, often to one decimal place, which makes it read as measured.
Policies and features that sound right. A refund window, a statutory notice period, a configuration option in a product. Plausible for that kind of thing, not true of this one.
Confident answers about your business. The model knows nothing about your company. Ask it something internal and it will answer from what is typical, presented as though specific.
Quotes nobody said. Attributed to a real person, in a register that person plausibly uses.
What reduces it You cannot eliminate this. You can substantially narrow it.
Give it the source. The single biggest lever. Paste the actual document rather than relying on what the model remembers. Working from text in front of it is a fundamentally different risk profile from working from training.
Constrain it explicitly. "Use only the text provided. If something is not covered, say 'not stated' rather than filling it in." This works better than people expect.
Permit uncertainty. "If you are not sure, say so." A simple instruction that genuinely changes output, because it makes hedging an acceptable pattern to follow.
Require evidence. "Quote the specific line that supports each claim." Now you can check the claim against the quote, and a fabricated claim usually comes with a quote that is not in the document.
Frame it as a draft. Asking for something to review produces different output than asking for a fact.
The mental adjustment Stop thinking of it as a system that knows things and occasionally errs. Think of it as a system that produces plausible text, which is frequently also true.
That reframing changes behaviour in the right direction. You stop expecting reliability and start building verification into how you work, which is the only approach that survives contact with a technology that is confidently wrong at unpredictable intervals.
The strange part is that the better these systems get, the more this matters. A model that is wrong ten percent of the time keeps you alert. A model that is wrong one percent of the time, in exactly the same confident voice, is far more likely to get something past you.

The Before-You-Send Checklist
Print it This is a page to keep beside you, not a post to read once. Pin it up for a month. After that you will be running it without noticing, and the checklist will have done its job.
Seven questions. Ten seconds each way. Keep it visible for your first month and it becomes automatic.
Before you prompt 1. Have I supplied the context it has no way of knowing? Background, constraints, the actual figures, the previous version. Anything in your head and not in the message is not available to it.
2. Have I specified what the output should look like? Format, length, tone, structure. Leave it open and you get an essay, because that is what an unspecified request resembles.
3. Does anything here count as confidential or identifiable? Customer details, employee information, credentials, unreleased numbers. If yes, either anonymize it or use a tool cleared for that level of sensitivity.
4. Did I ask for a draft, or demand a finished answer? Asking for something to review produces more honest output than asking for a definitive answer. The framing genuinely changes what comes back.
Before you send 5. Have I checked every fact, figure and citation? Every specific, against a primary source. This is the one that saves you.
6. Have I actually read every word? Not skimmed. Read. Skimming is how a fabricated number reaches a client inside an otherwise excellent document.
7. Am I comfortable standing behind this as my own? Because you will be. "The AI wrote that part" has never once improved a conversation.
Why a checklist rather than a rule "Be careful with AI output" is advice nobody can act on, because everyone already believes they are being careful.
A checklist is different. You either worked through it or you did not. It converts an intention into a fact about what you did.
The prompt-side questions take longer than they look, because two of them require you to decide something. That is the point. A surprising amount of weak output traces back to the person not having decided who it was for or what it needed to do.
The send-side questions take longer on a long document and are worth every minute.

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