POC readiness framework
Generative AI POC evaluation dataset: How UAE and Saudi enterprises should test a use case
A practical framework for building a representative, controlled evaluation dataset before a generative AI POC in the UAE or Saudi Arabia.
Executive answer
Build the evaluation dataset before choosing a winner. Define the business decision or workflow the AI will support, collect representative cases that the system will face, separate development examples from held-out test cases, agree scoring rules with business and risk owners, and require evidence against pre-agreed production gates. A POC should test performance in the intended operating context, not merely confirm that a model can produce impressive answers in a vendor demonstration.
The decision question: what evidence must a generative AI POC produce?
A serious POC should answer whether the proposed system can support a defined business workflow within the enterprise's risk tolerance. It should not be judged by generic prompts, a short demonstration, or an average quality score alone.
NIST's AI Risk Management Framework calls for documented testing and measurement that reflect conditions similar to the deployment setting. Its Generative AI Profile applies this lifecycle approach to generative AI. UAE guidance similarly calls for a trustworthy AI assessment adapted to the specific use case, while Saudi guidance emphasizes responsible use, privacy and security, reliability and safety, transparency, and accountability.
- Start with one bounded workflow, such as drafting responses to approved customer enquiries, summarising internal policy documents, or extracting information from standardised forms
- State what the AI may do, what it may recommend, and what remains a human decision
- Name the affected users, data owners, business owner, risk owner, and final go or no-go authority
- Define the intended production environment before deciding what to test
Use a six-part evaluation dataset, not one spreadsheet of prompts
A useful POC dataset combines normal work with the cases that expose operational and governance weaknesses. Each test item should include the input, any approved reference material, the expected outcome or scoring rubric, applicable restrictions, and a record of the reviewer decision.
Do not make every test item a question with one perfect answer. For many enterprise tasks, the right standard is whether the response is grounded in approved material, complete enough for the workflow, safely limited, correctly escalated, and usable by the intended employee.
- Routine cases: frequent, low-complexity examples that establish baseline workflow value
- Representative cases: examples reflecting the languages, document types, user roles, and business conditions expected in production
- Edge cases: incomplete records, conflicting source documents, ambiguous requests, unusual terminology, and exceptions
- Safety cases: requests that should be refused, redirected, or escalated under enterprise policy
- Adversarial cases: misleading instructions, attempts to override controls, irrelevant content, and attempts to induce unsupported answers or disclosures of restricted information,
Separate build data, calibration data, and decision data
The test evidence loses value when the same cases are repeatedly used to design prompts, select settings, or configure retrieval and then presented as final POC results. Keep distinct datasets for development, calibration, and final acceptance testing.
A held-out acceptance set should be controlled by the business and risk owners or an independent evaluation lead. NIST's recent AI technology evaluation approach uses inaccessible test data to reduce train and test contamination and support more objective measurement. The same principle is useful for an enterprise POC, even when the system is configured rather than trained.
- Development set: cases the delivery team may use to build prompts, workflows, retrieval, and guardrails
- Calibration set: a smaller set used to compare configuration options and improve the system
- Held-out acceptance set: cases reserved for the final go or no-go assessment
- Post-POC monitoring set: new cases retained for periodic testing after deployment
Define the scorecard before vendors run the POC
Agree scoring criteria and pass conditions before results are available. This protects the buyer from changing the definition of success after an attractive demonstration and makes competing vendors comparable.
NIST identifies both quantitative and qualitative measurement, documentation of test sets and metrics, and assessment by experts who are not front-line developers. For a business POC, that means combining operational metrics with structured human review by people who understand the workflow, records, policy, and consequences of error.
- Task success: did the output complete the intended step correctly enough for the stated workflow?
- Grounding: was each material claim supported by an approved source when source support was required?
- Safety and policy compliance: did the system avoid prohibited output, unauthorised disclosure, and unsafe instruction?
- Escalation quality: did it flag uncertainty, missing evidence, restricted cases, or requests outside its scope?
- Human effort: how much checking, rewriting, and rework did the output create?`,`Consistency: does the system perform acceptably across routine, difficult, and adversarial cases?
Set non-negotiable gates for regulated and high-impact workflows
Average performance can conceal unacceptable errors. Establish gates that apply to every relevant case category, particularly where the workflow concerns customers, employees, personal data, financial decisions, healthcare, public services, safety, or legal obligations.
UAE guidance applies its AI ethics guidelines to systems that make or inform significant decisions. Saudi AI Ethics Principles include questions on accountability, redress, continuous privacy and security monitoring, and periodic assessment. These sources support designing POC gates around the consequence of an error, not only around a model's overall score.
- No deployment if the system cannot reliably identify and escalate restricted or out-of-scope requests
- No autonomous action where a required human approval, legal review, or policy control has not been designed and tested
- No production use until the enterprise can explain the system's intended scope, accountable owner, review process, and incident route
- No production decision from results that depend on unapproved data, untraceable sources, or undocumented configuration changes
Run the POC as a controlled evaluation
Give each vendor or internal delivery team the same task definition, access rules, evaluation dataset structure, time window, and scoring rubric. Allow reasonable configuration work, but record the model version, prompts, retrieval sources, tools, safety settings, integrations, and human review design used for every evaluated run.
Review results at both item and workflow level. An output can be fluent but still fail because it is unsupported, omits a required condition, reveals restricted information, or gives the employee no clear next action. Capture the reason for every material failure so the enterprise can distinguish remediable configuration issues from unacceptable use-case risk.
- Freeze the final acceptance set before final testing
- Log system configuration and source-material versions
- Use blinded or independent review where practical
- Sample outputs for full audit, not only aggregate scoring
- Record failures by severity, cause, and affected control
Practical questions
How large should a generative AI POC evaluation dataset be?
Use enough cases to cover the target workflow, important user groups, common document patterns, difficult cases, and scenarios that must trigger refusal or escalation. The right size depends on the workflow and risk. Coverage and traceable review matter more than choosing an arbitrary number.
Can a vendor provide the POC test prompts?
A vendor can propose examples and test methods, but the buyer should own the final business scenarios, policy constraints, scoring rubric, and held-out acceptance set. Otherwise the POC may measure the vendor's prepared demonstration more than the enterprise's real workflow.
Should the evaluation dataset contain personal data?
Use the minimum data necessary for the POC and obtain the approvals required by the enterprise's applicable data governance and privacy obligations. Where realistic testing is possible with de-identified, synthetic, or otherwise controlled examples, assess whether that approach can reduce exposure while preserving the characteristics that matter to the workflow.
What is the minimum output from the POC?
Require a documented task definition, dataset inventory, scoring rubric, configuration record, item-level results, material failure log, risk and control assessment, operational ownership model, and a signed recommendation to proceed, redesign, extend the POC, or stop.
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