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Keep an AI performance-review prompt to the minimum evidence needed for the stated review: the employee’s role expectations, a defined period, and specific, verifiable work examples. Leave out sensitive medical or personal details, private information about other people, guesses about protected traits, unverified monitoring data, confidential material in an unapproved service, and instructions that ask the model to decide pay, promotion, discipline, or dismissal. Use the model, if permitted, to organise evidence and draft neutral language; a manager must verify the result and remain accountable for the assessment.
What this guidance covers—and what it does not
This is practical, cross-jurisdiction guidance rather than a universal legal rule. Whether an employer may process particular data, use a particular AI feature, or automate part of a review depends on the country or state, the purpose, the tool’s configuration, and the employer’s policy. The UK Information Commissioner’s Office (ICO) worker-monitoring guidance is currently under review following the Data (Use and Access) Act, so UK teams should check the current version and their own legal advice before operationalising a process.
The principles are consistent across the regulator material: collect only what is necessary, use accurate and job-related evidence, avoid discriminatory inferences, explain what information is used, let workers challenge it, and keep consequential decisions under accountable human review. The Australian Office of the Australian Information Commissioner (OAIC), the ICO, the European Commission AI Act Service Desk, and US employment agencies describe these principles in different legal settings.
Six categories that should stay out of the prompt
1. Sensitive personal and medical information that the review does not need
Do not paste diagnoses, treatment details, disability information, medication, family circumstances, pregnancy information, mental-health history, or other sensitive personal details merely to make a prompt appear complete. If a workplace adjustment affects how a goal should be assessed, handle that information through the employer’s authorised accommodation or HR process; do not expose the underlying medical detail to a general-purpose model.
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Data minimisation is the relevant test: ask whether each field is necessary to assess a defined job expectation. The ICO applies minimisation to worker monitoring, and OAIC guidance recommends, as best practice, not entering personal information—particularly sensitive information—into publicly available generative-AI services. Removing a name does not automatically make medical or distinctive personal details safe: combinations of dates, incidents, and circumstances can identify a person.
2. Private information about identifiable coworkers, customers, or subordinates
A review prompt should not include another person’s name, contact details, complaint, health information, performance issue, or private conversation unless that information is strictly necessary, authorised, and processed through an approved workflow. A manager may need to describe a customer-impacting event, for example, but can usually state the verified work fact without reproducing the customer’s identity or unrelated personal history.
Use de-identification where workable and retain only the detail needed to understand the employee’s contribution. If the identity of another person is essential to a formal investigation, stop and use the organisation’s investigation or HR channel instead of copying the case into an unapproved chat tool. Privacy and confidentiality obligations can apply to information about people who are not the review subject.
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3. Requests to guess protected or sensitive traits
Never ask a model to infer disability, health, race, religion, age, pregnancy, sexual orientation, or another protected or sensitive characteristic from writing style, absences, work hours, keystrokes, communication patterns, location, or any other proxy. Prompts such as “Could this employee have a condition that explains the missed deadlines?” or “Estimate cultural fit from these messages” invite an inference that is both unreliable and potentially discriminatory.
The ICO explains that a system can reproduce discrimination through proxy patterns even when the protected field is absent. An inference can itself become sensitive data when it reveals enough about a person. Therefore, deleting an explicit field such as “disability” does not cure a prompt that contains data from which the model is being asked to reconstruct it. Ask about observable, job-related evidence instead: “Which agreed deliverables were late, what was the documented impact, and what context is missing?”
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4. Unverified monitoring data, bare scores, or analytics treated as facts
Do not feed a prompt with keystroke counts, login or availability metrics, webcam signals, location trails, sentiment scores, productivity dashboards, or an unexplained “efficiency” number and present it as proof of performance. Monitoring systems measure a selected activity, not necessarily the quality, complexity, collaboration, or outcome of the work. Analytics can be incomplete, misconfigured, or misleading.
Before any monitoring-derived item is considered, establish what it measures, its time window, known error modes, and whether the employee had a fair opportunity to explain it. The ICO states that monitoring information must not be incorrect or misleading and that employees should be able to see and challenge information used in performance reviews. A model cannot validate a metric merely because it is expressed as a number.
Replace a bare score with the underlying, corroborated evidence: the agreed target, the dated result, the source record, and any material context. If the source cannot be checked, mark the item as unverified and exclude it from the assessment rather than asking the model to fill the gap.
5. Instructions to make the employment decision
A prompt should not tell an AI system to decide who gets fired, rank employees for promotion, set a pay score, select candidates for redundancy, or determine a disciplinary outcome. Those are consequential employment decisions, not clerical summarisation. A model may produce a confident ranking from biased or incomplete inputs, while obscuring who made the decision and how an employee can challenge it.
European Commission AI Act Service Desk guidance identifies employment evaluation and decisions affecting work relationships as potentially high-risk AI uses. US Equal Employment Opportunity Commission (EEOC) and Department of Justice material describes disability-discrimination risks when software is used for monitoring, pay, promotions, or other employment decisions. Keep the decision with an appropriately authorised human who has reviewed the evidence, relevant policy, and the employee’s response.
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You can ask an approved tool to group documented examples by competency, identify missing evidence, or suggest questions for a review meeting. You cannot delegate the judgment, the weighting of competing facts, the accommodation analysis, or the final employment action to the model.
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Do not paste internal strategy, product roadmaps, source code, credentials, security information, customer records, contracts, investigation files, or complete employee files into a public or otherwise unapproved AI service. A prompt may be retained, reviewed by the provider, used for service improvement, or exposed through account and access weaknesses depending on the configuration and contract.
“The tool is popular” is not an approval. Confirm the exact service, account type, workspace controls, administrator access, retention period, model-training or reuse setting, regional processing, and deletion process. OAIC guidance notes that privacy obligations apply to personal information entered into AI products and recommends against entering personal or sensitive information into publicly available generative AI. If the employer has no documented answer for how submitted data is handled, do not submit it.
A safer prompt pattern
A useful prompt is narrow, evidence-led, and explicit about the model’s limited role. Use a pseudonymous employee reference where practical, the review period, the role expectations, and a small set of current examples that the manager is authorised to use.
Role: [role title and approved expectations]
Review period: [start date] to [end date]
Evidence (job-related and verified):
- [dated example, source, result, and impact]
- [dated example, source, result, and impact]
Context already confirmed through the authorised process: [brief, non-sensitive context]
Task: Organise these examples under the stated expectations. Identify missing or conflicting evidence and draft neutral questions for the review conversation. Do not infer causes, health, disability, protected traits, intent, or personality. Do not recommend pay, promotion, discipline, or termination. Do not add facts not present in the evidence.
The bracketed fields are a format example, not permission to insert unnecessary personal data. Replace them only with information the manager is authorised to process in the approved system.
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Six steps from raw notes to a defensible draft
- Define the purpose. State whether the tool is organising evidence, checking coverage against expectations, or helping draft questions. A vague purpose encourages unnecessary collection.
- Check authorisation and the tool. Confirm that the employer permits this use, the account is the approved one, and the vendor settings and contract cover the data involved. Record retention, access, reuse, and deletion conditions before submission.
- Minimise and de-identify. Remove medical and family details, protected-trait clues, credentials, customer or coworker identifiers, and unrelated history. Keep only the role expectation, period, and evidence needed for the stated purpose.
- Verify each item. Trace a claim to its source, confirm the date and measurement, and distinguish an observed result from an interpretation. Exclude unverified monitoring outputs and unsupported allegations.
- Constrain the request. Tell the model to organise, flag gaps, and use neutral language. Prohibit inferences about traits or causes and prohibit recommendations about employment outcomes.
- Review as the decision-maker. Compare every generated sentence with the original evidence, add context the tool could not know, correct omissions, and remove unsupported claims. Give the employee a meaningful way to see and challenge information used in the review, as required by applicable policy or law. Make and document the final assessment yourself.
What to include instead
| Include | Why it belongs | Quality check |
|---|---|---|
| Role expectations or competency definitions | They establish the job-related standard. | Use the current, approved version for the review period. |
| Defined start and end dates | They prevent unrelated history from entering the assessment. | Confirm the dates match the organisation’s review cycle. |
| Dated, specific work examples | They let the reviewer test an observable result. | Identify the source, result, and material impact. |
| Documented feedback that the manager is authorised to use | It adds relevant perspectives without exposing unnecessary identities. | Remove private details and check that the feedback is attributable and current. |
| Known gaps or conflicting records | They stop the model from presenting uncertainty as fact. | Ask for questions to resolve the gap, not a guessed explanation. |
| Neutral output instructions | They keep the tool in a support role. | Require source-grounded language and no trait or outcome inference. |
Common prompt failures and their fixes
“Use everything available about this employee.”
This has no necessity limit and invites medical, family, private, and unrelated information. Replace it with a named review period, stated expectations, and a short evidence list.
“Their activity score is low—explain why.”
The wording treats a possibly flawed metric as fact and asks for a causal or medical inference. Verify what the score measures, present only corroborated work outcomes, and ask what evidence is missing.
“Remove the people with the lowest predicted potential.”
This delegates a high-impact employment decision and may encode proxy discrimination. Use the tool, if approved, to organise documented results; have an accountable human apply the published criteria and review individual context.
“Here is the customer complaint, including the customer’s contact details.”
It discloses another person’s private information without showing that it is necessary. Summarise the verified service issue without identifying details, or use the authorised case-management process.
“The model says the employee lacks commitment.”
That is an unsupported interpretation, not an observation. Replace it with the specific missed commitment, date, agreed expectation, impact, and the employee’s response.
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“This is an internal account; confidentiality is guaranteed.”
An account label is not a contractual or technical guarantee. Check the actual workspace settings, provider terms, retention, access, and reuse controls; if they are unknown, do not upload the material.
Use a decision check before pressing submit
| Question | If the answer is no |
|---|---|
| Is this exact AI service and account approved for performance-review data? | Stop and use an approved workflow. |
| Is every field necessary for the stated review purpose? | Delete it or explain why the authorised process requires it. |
| Is the evidence job-related, current, and verifiable? | Verify it, label the uncertainty, or leave it out. |
| Could the prompt reveal or invite an inference about a protected or sensitive trait? | Rewrite around observable work evidence. |
| Could another identifiable person be harmed by disclosure? | De-identify or move the matter to the appropriate secure process. |
| Does the prompt ask the model to decide an employment outcome? | Remove the instruction and retain human decision authority. |
| Can the employee understand and challenge information used? | Follow the applicable transparency, access, and review procedure before relying on it. |
| Do you know how the service stores, accesses, reuses, and deletes prompts? | Obtain the policy and contractual answer or do not submit. |
Jurisdiction and policy checks managers still need to make
General principles cannot answer employer-specific questions such as whether a particular monitoring metric is lawful, whether an accommodation record may be used, how long prompts may be retained, or which administrator can access a workspace. Ask HR, privacy, security, and legal teams for the applicable rule before using AI in a review.
- United Kingdom: Consult the current ICO material on data protection and monitoring workers, noting its stated review after the Data (Use and Access) Act, and the ICO explanation of fairness, bias, and discrimination.
- European Union: Check whether the planned feature falls within employment-related high-risk AI uses described by the European Commission AI Act Service Desk and follow the organisation’s AI governance process.
- United States: Review EEOC and Department of Justice material on disability discrimination and software used for monitoring, pay, promotion, or other employment actions. The EEOC’s ADA primer also emphasises that actual job performance is a better measure of ability than disability and that disability-related inquiries are constrained.
- Australia: Apply OAIC privacy guidance to personal information entered into commercial AI products, with particular caution for publicly available services.
These references explain the direction of the obligations; they do not replace jurisdiction-specific advice or the employer’s approved-tool policy.
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AI can reduce clerical work when it receives a small, authorised, accurate evidence set and is instructed to organise rather than judge. It should not become a repository for sensitive biographies, a decoder of protected traits, a substitute for fact-checking, or an invisible decision-maker. The manager remains responsible for the evidence selected, the interpretation applied, the opportunity given to challenge it, and the final performance decision.
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