AI · 2.5 HOURS
AI Output Verification
AI Output Verification teaches professionals who pass on AI-generated text under their own name to check every claim against a source before it leaves their desk. You finish with a verification note a colleague can follow and repeat, and your organisation sends out fewer confident answers that turn out to be wrong.
About this course
AI tools write by predicting likely words, so a wrong figure, a reversed condition, or an invented reference arrives in the same calm, confident voice as a correct one. Anyone who passes AI output on under their own name therefore needs a reliable way to check it against the documents it claims to rest on, rather than judging it by how certain it sounds.
The course teaches a four-step check: list every claim, trace each one to a source with authority over it, test whether the reasoning goes further than the sources allow, and decide whether each claim can be used or must be held. You finish with a signed verification note on a real piece of AI output, written so that a colleague could repeat your check without asking what you did.
What you will take away
- You will be able to explain why the tone of an AI-written sentence tells you nothing about whether it is correct, and to compare it with its source instead.
- You will be able to list every checkable claim in a piece of output, including a figure or date hidden inside a sentence that reads like a linking phrase.
- You will be able to trace a claim to a source with authority over it, such as the signed contract, the published guidance, or the system of record, and name the exact place that confirms it.
- You will be able to spot a conclusion that widens the scope, turns a pattern into a cause, turns a possibility into a certainty, or drops a condition the source attached.
- You will leave with a signed verification note that records the claims, where each was traced, what the reasoning test found, and whether each claim was used or held.
Who this course is for
- Analysts and consultants who receive AI-drafted summaries of reports, contracts, or research and must stand behind every figure they pass on.
- Account managers who send clients letters and updates that mention dates, terms, and policies, where one wrong detail becomes a commitment.
- Policy officers and legal and compliance support staff who work from guidance, clauses, and case references that a model may misquote or invent.
- Managers who review work that colleagues produced with AI tools and want a clear record of what was checked, against what, and what was held back.
What you will do
Each lesson teaches one part of the method, works through a realistic example, and ends with a check on a case you have not seen. The course closes with an assessment and then the piece of work you sign.
- 5lessons with a worked example and a check
- 7scenario questions in the course assessment
- Signedthe verification note, with a record anyone can verify
- 2.5 hoursat your own pace, with progress saved
- 01
Lesson
How confident error happens
This lesson explains how a language model produces a sentence that sounds certain and is wrong, and why checking only the hedged sentences is the wrong way round. You read a staff travel policy and mark each sentence of a model's summary as matching the source or not in the source.
- How a model produces a sentence
- What this lesson is not saying
- Two labels for comparing with a source
- The mistake people usually make
You mark 3 statements from a realistic case and receive an explanation for each one.
- 02
Lesson
List the claims
This lesson introduces the four-step check and teaches you to separate checkable claims, such as figures, dates, names, and commitments, from framing that has nothing to trace. You mark each sentence of a drafted project update, including a claim hidden inside a sentence that reads like a transition.
- The four-step check
- What a claim is
- What framing is
- Listing is not checking, and the claim hidden in a link
You mark 4 statements from a realistic case and receive an explanation for each one.
- 03
Lesson
Trace each claim
This lesson teaches you to trace a claim to a source with authority over it and to record the source, the place, and what it says, because a citation the model supplied is only a lead. You compare two colleagues' traces of a notice period and choose the one you would accept.
- What tracing is
- A source with authority
- What tracing is not
- Traced and not traced
You compare two versions of the same piece of work, choose the stronger one, and see the reasoning behind the answer.
- 04
Lesson
Test the reasoning
This lesson shows the four ways a conclusion goes further than its sources while every fact inside it remains correct, including widened scope and dropped conditions. You mark each sentence a model wrote from an internal pilot report as following from the source or going further than it.
- What testing the reasoning means
- Four ways a conclusion goes further
- Two labels for a conclusion
- What this step is not, and the usual mistake
You mark 3 statements from a realistic case and receive an explanation for each one.
- 05
Lesson
Judge three outputs
This lesson adds the final step, deciding whether each claim can be used or must be held, and explains why running out of time is always a reason to hold. You apply that decision to sentences from a board summary, a customer reply, and a legal case list.
- Use or hold
- Holding a claim is not rejecting the output
- The decision does not depend on how important the claim feels
You mark 4 statements from a realistic case and receive an explanation for each one.
- 06
Course assessment
Course assessment
This lesson recaps all four steps and works one short piece of supplier output through the whole check, from listing the claims to the final decision. You then answer seven scenario questions set in situations you have not seen before, and you need six correct answers to pass.
- Why confident error needs a method
- List, then trace
- Test, then decide
You judge 7 new workplace situations, with feedback on every option, and need 6 correct to pass.
- 07
Final work and signed record
The verification note
This lesson explains what a verification note must contain and why a line such as 'reviewed with AI' tells the reader nothing. You run the four-step check on a real piece of AI output of your own and write the signed note that your record will show.
- What a verification note is
- What a verification note is not
- Write each part so a colleague could repeat it
- How the note is checked
You write the verification note for your own work, part by part, and sign it as your record.
GOOD WORK.THE VERIFICATION NOTE, SIGNED.
WHAT CHANGES
What the course changes for you and your organisation.
- 01
Confident errors caught before sending
The four-step check finds the one wrong figure, clause, or date in an otherwise sound summary before it reaches a client or a board. Your organisation sends out fewer confident answers that later have to be corrected.
- 02
Checks a colleague can repeat
Each trace names the source, the place in it, and what it says, so a colleague or manager can open the same document and reach the same decision. That turns a private sense of having checked into a record the team can rely on and audit.
- 03
Useful output kept, weak claims held
The course teaches you to hold individual claims rather than reject a whole output because one sentence is wrong. Good work produced with AI is kept, and only the sentences that cannot be supported are corrected or removed.
- 04
Every claim traced to a source
The Stanford AI Index 2026 reports hallucination rates between 22% and 94% across 26 leading models on a new accuracy benchmark. Any organisation that sends AI assisted text out under a person’s name needs someone who checks each claim before it leaves the building.
ROLES THIS SKILL SHOWS UP IN
Roles where these skills are already valued and paid for.
These figures are published salary bands and wage premiums, not a guarantee that finishing this course moves you to the top of the range. They are here so you can see what the market is already paying for the work you will practise.
AI Prompt Engineer
£62,750 to £115,000, midpoint £92,500
Robert Half's 2026 UK guide. London midpoint is £125,750.
Artificial Intelligence Engineer
Midpoint £65,750
The neighbouring engineering role in the same Robert Half guide, for people who take the brief into a built system.
Machine Learning Engineer
Midpoint £75,000
Same guide. The brief you write here is the instruction that role then tests.
AI enablement or operations lead
Premium on the role you already hold
PwC found specialist AI skills carried a 34.2% wage premium in 2025. Many of those postings sit inside operations, customer, and people teams rather than a lab.
Customer communications or account work
The same seat, with a skill the posting now names
The course artefact is a prompt card a colleague can run. That is the work those teams are hiring people to supervise.
Learning, knowledge, or product operations
Listed against the AI-skilled premium
These roles now ask for someone who can brief a model, check the output, and leave a reusable instruction. That is the check this course marks.
QUESTIONS
Questions about AI Output Verification
- What is AI Output Verification about?
- AI tools write by predicting likely words, so a wrong figure, a reversed condition, or an invented reference arrives in the same calm, confident voice as a correct one. Anyone who passes AI output on under their own name therefore needs a reliable way to check it against the documents it claims to rest on, rather than judging it by how certain it sounds. The course teaches a four-step check: list every claim, trace each one to a source with authority over it, test whether the reasoning goes further than the sources allow, and decide whether each claim can be used or must be held. You finish with a signed verification note on a real piece of AI output, written so that a colleague could repeat your check without asking what you did.
- Who is AI Output Verification for?
- Analysts and consultants who receive AI-drafted summaries of reports, contracts, or research and must stand behind every figure they pass on. Account managers who send clients letters and updates that mention dates, terms, and policies, where one wrong detail becomes a commitment. Policy officers and legal and compliance support staff who work from guidance, clauses, and case references that a model may misquote or invent. Managers who review work that colleagues produced with AI tools and want a clear record of what was checked, against what, and what was held back. No specialist background is assumed, and every term is explained before it is used.
- What will I be able to do after AI Output Verification?
- You will be able to explain why the tone of an AI-written sentence tells you nothing about whether it is correct, and to compare it with its source instead. You will be able to list every checkable claim in a piece of output, including a figure or date hidden inside a sentence that reads like a linking phrase. You will be able to trace a claim to a source with authority over it, such as the signed contract, the published guidance, or the system of record, and name the exact place that confirms it. You will be able to spot a conclusion that widens the scope, turns a pattern into a cause, turns a possibility into a certainty, or drops a condition the source attached. You will leave with a signed verification note that records the claims, where each was traced, what the reasoning test found, and whether each claim was used or held.
- How long does the course take, and how is it taught?
- The course takes about 2.5 hours and is studied online at your own pace. It has 5 lessons, each with a worked example, a practice exercise, and a check on a new case, followed by a course assessment of 7 scenario questions in which you need 6 correct to pass. Your progress is saved to your account.
- Do I get a certificate?
- Yes. When you pass, you sign a record that names you, the course, and the verification note. Anyone you share it with can verify it online and download it as a PDF. The record confirms what you completed and does not claim compliance with any regulation.
- How much does it cost, and when can I start?
- The course costs £99, paid once by card through Stripe. Access begins as soon as payment is confirmed, and you can save a sign-in to return to the course from any device.
- Can my organisation train a whole team?
- Yes. Individuals can buy any self-paced course online, and organisations can book trainer-led courses for teams, in person or online, through the Experrt Academy.
More ai courses are listed on the self-paced ai courses for professionals page.
THERE IS A NEXT CHAPTER.
Start today, and finish with
the verification note your organisation can use.
Checkout takes an email address and a card, and access begins as soon as payment is confirmed. When you finish, you sign a record that names you and the verification note, which anyone you choose can verify online. The record confirms what you completed and does not claim compliance with any regulation.