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Do not build an AI-generated drawing, CAD model, calculation or specification simply because it looks plausible. Treat it as a proposed design—a hypothesis that must be connected to controlled requirements, engineering analysis, inspection, testing and evidence. A sound validation process first establishes what the product must do and for whom, then proves both that the design meets its stated requirements and that it is the right solution for its real operating context.
The exact checks depend on the discipline, jurisdiction, materials, loads and consequences of failure. This guide provides a systems-engineering workflow, not a substitute for applicable codes, regulatory approval, hazard analysis or licensed professional judgment.
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Verification and validation answer different questions
NASA’s Systems Engineering Handbook gives a useful distinction: “While verification proves whether ‘the product was done right,’ validation proves whether ‘the right product was done.’” (NASA, SEH 5.0 Product Realization.) Both activities are required for an AI-generated engineering design.
| Activity | Question | What is checked | Typical evidence |
|---|---|---|---|
| Verification | Did the design or realized product meet its specified requirements? | Dimensions, strength limits, interfaces, performance thresholds, materials, software or electrical behavior and other documented requirements. | Analysis, inspection, demonstration or test results traced to each requirement. |
| Validation | Does this product meet stakeholder expectations and function as intended in its operating environment? | Usefulness, human interaction, environmental conditions, workflow, maintainability, unintended effects and system-level behavior. | Representative scenarios, user or operator evaluation, realistic tests, field trials and documented acceptance decisions. |
A design can pass a dimensional or mathematical check and still be unusable, inaccessible, impossible to manufacture, incompatible with surrounding equipment or unsafe in the environment where people will operate it. NASA frames validation around stakeholder expectations and the concept of operations, with evaluation in realistic or simulated conditions. Read the related fundamentals guidance in NASA’s SEH 2.0 Fundamentals of Systems Engineering.
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1. Define intended use before reviewing the generated design
Start with the problem and operating context, not with the AI output. Write a short concept of operations that describes the complete use scenario:
- Who operates, maintains, installs or is affected by the product?
- What task must it perform, how often and for how long?
- Where will it operate: indoors or outdoors, at altitude, in heat, cold, moisture, vibration, dust, radiation or corrosive conditions?
- What are the input materials, energy sources, connected systems and human interfaces?
- What must the product never do, and what happens if it fails?
- What legal, industry, customer, accessibility, cybersecurity or environmental constraints apply?
Convert the answers into measurable requirements. “Easy to use” might become a maximum setup time for a defined user, a force limit, a readable display requirement or a permitted error rate. “Strong enough” needs a specified load case, safety factor, service life, failure mode and acceptance limit. NASA asks, “Are we building the right product for our users and other stakeholders?” (NASA SEH 2.0 Fundamentals.) Use that question to expose requirements that an AI prompt may have omitted.
Separate mandatory requirements from preferences
Mark each requirement as mandatory, preferred or informational. Identify the source—contract, regulation, interface control document, user need, engineering standard or explicit design decision—and assign an owner who can accept or reject evidence. A generated model should not be allowed to silently convert a preference into a safety limit or a missing requirement into an assumption.
2. Freeze a reviewable design baseline
Validation is impossible if reviewers cannot identify exactly what was assessed. Create a controlled baseline before analysis or fabrication. Record:
- The exact CAD files, drawings, bills of material, schematics, code, generated specifications and calculation files.
- The AI system, model version if available, prompt or source inputs, attached reference documents and generation date.
- Units, coordinate systems, material and process choices, tolerances, load cases, environmental assumptions and operating limits.
- External interfaces, revision identifiers, configuration items and dependencies.
- Who generated, reviewed and approved each item, with dates and unresolved issues.
Put a read-only identifier or revision on every reviewed artifact and link each calculation, inspection record and test result to that identifier. If a dimension, material, boundary condition or prompt changes, open a new revision and determine which analyses and tests must be repeated. NASA’s product-realization guidance emphasizes a verified baseline and configuration control because uncontrolled changes can invalidate evidence and create costly problems (NASA SEH 5.0 Product Realization).
3. Build a requirement-to-evidence matrix
Before choosing tools, map every requirement to a verification or validation method. The matrix is the control document that prevents attractive AI-generated features from distracting reviewers from missing evidence.
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| ID | Requirement or expectation | Method | Procedure and configuration | Acceptance criterion | Result and disposition |
|---|---|---|---|---|---|
| R-001 | Interface fits the specified mating component. | Inspection and demonstration | Measure the controlled prototype and assemble it with the released mating part. | All specified dimensions and engagement conditions pass. | Record measured values, photographs, anomalies and approval. |
| R-002 | Supports the defined operating load for the required life. | Analysis plus test when warranted by risk. | Use the approved material data, load cases, boundary conditions and test configuration. | Stress, deflection, fatigue or other limits remain within the requirement. | Link the calculation, model review and test report to the baseline. |
| R-003 | Operator can complete the task safely in the intended environment. | Validation demonstration or representative test. | Use representative operators, equipment, procedures and environmental conditions. | Task completion and safety criteria pass without prohibited workarounds. | Capture observations, deviations, user feedback and decision. |
NASA identifies analysis, inspection, demonstration and test as distinct verification methods and notes that the appropriate choice depends on lifecycle phase and the product’s position in the system (NASA Systems Engineering Handbook Appendix). For each row, document the procedure, configuration, measured or observed result, acceptance decision and any anomaly. Objective evidence and closure of discrepancies are part of completing verification.
4. Review the AI output as an engineering model, not an authority
An AI system may produce coherent geometry and confident explanations while omitting a load case, confusing units, inventing a material property or assuming an interface that does not exist. Review the source of every consequential input.
Check geometry and interfaces
- Confirm that dimensions, datums, tolerances, clearances and fits use one consistent unit system.
- Check that fasteners, welds, seals, bearings, connectors, sensors and service access are physically reachable and compatible with adjacent parts.
- Look for impossible assembly sequences, trapped components, inaccessible inspection points and unmanufacturable features.
- Verify mass properties, center of gravity, cable or hose routing, thermal paths and movement envelopes where they affect the system.
Check materials and processes
- Verify material grade, condition, allowable properties, temperature range, corrosion behavior and compatibility with fluids or neighboring materials.
- Confirm that the proposed manufacturing process can hold the tolerances and produce the specified surface finish, porosity, heat treatment or joining quality.
- Check whether material properties are temperature-, rate-, orientation- or life-dependent rather than a single generic value.
Check loads, boundaries and simplifications
- List static, dynamic, impact, pressure, thermal, vibration, acoustic, electromagnetic, seismic, transport, installation and misuse loads that apply.
- Inspect supports, constraints, contact definitions, friction, damping, preload, mesh or discretization choices and initial conditions.
- Challenge every symmetry, linearity, rigid-body, small-deflection, steady-state or ideal-interface assumption.
- Check worst credible combinations, transient behavior, tolerances, degradation, uncertainty and failure modes.
Ask a qualified engineer in the relevant discipline to review assumptions independently when consequences or uncertainty are significant. An AI-generated calculation is an input to that review, not an approval signature.
5. Choose evidence that matches the claim
Do not select a method because it is convenient or because a simulation produces a polished plot. Compare each candidate method on the requirement it addresses, realism, traceability, uncertainty, consequence of a missed defect and resources required.
| Method | Best suited to | Strength | Limits and cautions |
|---|---|---|---|
| Analysis | Requirements that can be evaluated from validated equations, calculations or engineering models. | Can explore many cases before committing material and can expose sensitivities. | Only as credible as inputs, assumptions, boundary conditions, material data and model applicability. Independent review may be needed. |
| Inspection | Observable physical or documentary characteristics such as dimensions, finish, labeling, workmanship and presence of parts. | Direct, relatively quick evidence for features that can be observed or measured. | Does not establish hidden behavior, life, strength or user suitability by itself. |
| Demonstration | Showing that a user, operator or system can perform a defined function under controlled conditions. | Useful for operational sequences, interfaces and visible behavior. | Can miss long-term, extreme or internal failure modes unless the scenario is representative. |
| Test | Behavior that requires measured response under specified inputs and environments. | Produces empirical data from prototypes or final products at defined points. | Costs more time and equipment; poor fixtures, instrumentation or unrepresentative conditions can make results misleading. |
NASA describes testing as a way to obtain detailed behavioral data and notes that tests may use prototypes as well as final products (NASA SEH 5.0 Product Realization). Use the least complex method that directly answers the requirement, then add independent evidence when the risk of a missed defect is high.
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Simulation can reveal interference, thermal trends, fluid behavior, structural response, control interactions or other issues before material is committed. NASA describes modelling and simulation as analytical tools and distinguishes testing as a more resource-intensive way to obtain behavioral data at specified points (NASA Systems Engineering Handbook Appendix).
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A simulation does not, by itself, prove real-world safety. Before relying on a result, reviewers should establish:
- The model represents the released geometry, materials, interfaces and operating configuration.
- Inputs come from traceable measurements, approved data or justified engineering values.
- Boundary conditions and loads represent the requirement, including transients and credible extremes.
- The numerical method, mesh or discretization, convergence, tolerances and sensitivity are adequate for the decision.
- The model has been compared with relevant analytical solutions, historical data or physical measurements where possible.
- Uncertainty, model-form error and out-of-range behavior are stated alongside the result.
Escalate to physical testing or specialist review when a requirement concerns a failure mode the model cannot represent reliably, when uncertainty is large relative to the margin, or when the consequences of a wrong conclusion are severe. A simulation may narrow the test envelope; it does not eliminate the need for a test that the requirement or governing standard calls for.
7. Inspect and test a prototype proportionate to risk
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Plan the test before turning on the equipment
- State the requirement, objective, test article revision, configuration and pass/fail criterion.
- Define loads, inputs, environmental conditions, duration, sequence, instrumentation, sampling and calibration.
- Identify hazards, protective measures, abort limits and who is authorized to operate the test.
- Record baseline condition, setup photographs, instrument identifiers and any deviations before the run.
- Capture raw data as well as processed plots, observations, failures and post-test inspection results.
- Have an independent reviewer confirm that the result supports the requirement and that anomalies are dispositioned.
Use prototypes when they represent the relevant design and manufacturing process. If the prototype differs from the intended product, document which conclusions transfer and which do not. A successful test on a benign configuration cannot be generalized to an untested load, environment or revision.
8. Validate in the environment and workflow that matter
Validation asks whether the product works for its stakeholders, not merely whether a laboratory setup produced an acceptable number. Recreate the representative environment, interfaces, procedures and constraints whenever practical. Include anticipated users or operators; NASA’s validation process recommends user and operator participation whenever possible and calls for planned validation, result analysis, reporting and preservation of work products (NASA SEH 5.0 Product Realization).
Make the scenario representative
- Use the real or representative equipment, consumables, software versions and communication links.
- Include startup, normal operation, maintenance, cleaning, fault recovery, shutdown and handoff conditions.
- Test foreseeable misuse and human-factor hazards without exposing people to uncontrolled danger.
- Observe workload, visibility, reach, noise, heat, alarms, instructions, accessibility and error recovery.
- Record what users actually do, not only whether they say the design is acceptable.
Simulated conditions are useful when real operation is unsafe, unavailable or expensive, but state the differences and assess how they affect the conclusion. A user demonstration cannot establish hidden fatigue life; a strength test cannot establish that operators can safely perform the task.
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9. Treat anomalies as controlled engineering work
A failed check, unexpected deformation, unstable control response, missing requirement or user workaround is evidence that the design or the evidence plan needs attention. Do not erase an anomaly by editing a result or quietly changing the acceptance criterion.
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- Contain the risk: stop testing, quarantine hardware, restrict operation or add safeguards when necessary.
- Determine whether the cause is a design defect, manufacturing variation, instrumentation problem, procedure error, model limitation or misunderstood requirement.
- Approve a corrective change through configuration control.
- Repeat every affected analysis, inspection, demonstration or test, and identify evidence that remains valid.
- Close the anomaly only when an authorized reviewer accepts the objective evidence and residual risk.
Retain the requirement matrix, procedures, raw and summarized results, model assumptions, review minutes, deviations, anomaly records and final approval. NASA describes preserving validation work products and resolving discovered problems before delivery (NASA SEH 5.0 Product Realization).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.10. Add controls specific to AI-assisted design
AI governance helps you manage how the design was generated and evaluated, but it does not replace engineering evidence. NIST’s AI Risk Management Framework (AI RMF) is voluntary and addresses trustworthiness considerations in the design, development, use and evaluation of AI systems. NIST released AI RMF 1.0 on January 26, 2023, released its Generative AI Profile on July 26, 2024, and says AI RMF 1.0 is being revised.
Apply AI-specific controls to the design workflow:
- Preserve prompts, reference files, model identifiers, settings, generated alternatives and human edits so the provenance of each design decision is clear.
- Check generated dimensions, units, citations, equations, code and material data against authoritative engineering sources rather than accepting fluent explanations.
- Prevent confidential drawings, export-controlled information, personal data or proprietary specifications from entering tools that are not approved for that information.
- Require a named engineer or accountable design authority to approve requirements, assumptions, evidence and deviations.
- Separate AI-generated suggestions from released requirements and configuration items in your document system.
- Re-run reviews when the model, prompt, source data or generated artifact changes.
The NIST AI Resource Center provides documents, tools and guidance for AI testing, evaluation, verification and validation. These resources can strengthen governance and evidence practices; they are not a complete product-safety approval route for every engineering design.
Where TEVV-Athlon fits
NIST’s TEVV-Athlon Framework for Evaluating AI Systems describes a customizable four-stage method for assessing AI systems and says it can apply to statistical machine learning, large language models, multimodal models and agentic systems. The page states that its draft comment period opened August 7, 2026 and closed October 6, 2026. Use such a framework to evaluate the AI tool’s behavior, test coverage and governance; do not present it as proof that a particular physical design is safe.
11. Scale review depth to consequence and uncertainty
Not every generated bracket, enclosure or control algorithm needs the same evidence package. Classify the design according to the consequence of failure, uncertainty in the model and difficulty of detecting a defect before use.
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| Situation | Minimum sensible controls | Escalation triggers |
|---|---|---|
| Low consequence, noncritical prototype or fit-check part | Controlled baseline, requirement list, dimensional inspection and basic functional demonstration. | Unexpected loads, user exposure, hidden failure modes or transition toward production. |
| Moderate consequence or significant energy, heat, motion or service life | Independent assumption review, traceable material data, analysis, prototype inspection, planned test and anomaly closure. | Model uncertainty exceeds margin, test article is not representative, or applicable standards require specific qualification. |
| High consequence, safety-critical, regulated or public-facing system | Formal hazard analysis, qualified engineering authority, applicable codes and regulatory review, independent verification, representative qualification testing and controlled records. | Any unresolved requirement, unexplained anomaly, unverified interface or reliance on an out-of-scope model. |
This table is a planning aid, not a universal safety classification. The governing discipline, authority having jurisdiction, contract and risk process determine the required level of assurance.
12. Recognize common failure modes before they become build decisions
“The model looks professional, so it must be correct”
Visual polish is not evidence. Require traceable inputs, independent calculations and checks against the requirement matrix.
“The prompt included all the requirements”
Prompts often omit lifecycle, maintenance, environmental, interface and failure requirements. Interview stakeholders and inspect the concept of operations before accepting the generated specification.
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A plot can hide incorrect constraints, mesh sensitivity, material data or load cases. Review model validity and obtain physical evidence when the requirement depends on behavior the model cannot establish.
“A prototype test passed, so every revision is approved”
Configuration changes can invalidate the result. Link each result to a revision and assess retest needs after every change.
“Users liked the demonstration, so the design is verified”
User acceptance supports validation but does not prove dimensions, strength, durability, electrical compliance or other technical requirements. Keep verification and validation records distinct but connected.
“The AI framework is an engineering certification”
NIST’s voluntary AI RMF and evaluation resources help govern AI systems. They do not replace domain standards, hazard analysis, licensed review, qualification testing or regulatory approval.
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Before authorizing fabrication, procurement or operational use, an accountable reviewer should be able to answer yes to all applicable questions:
- Are intended users, operating environments, interfaces, constraints and failure consequences documented?
- Is the exact design baseline, including AI provenance and revision, frozen and identifiable?
- Does every requirement have an appropriate verification or validation method and acceptance criterion?
- Have assumptions, units, materials, boundary conditions, load cases and model limitations been independently checked?
- Are analysis, inspection, demonstration and test results traceable to the baseline and supported by objective evidence?
- Were representative users, operators and environments included where the requirement depends on real use?
- Are anomalies, deviations and residual risks documented, approved and closed or explicitly accepted?
- Have applicable codes, standards, hazard analyses, specialist reviews and regulatory obligations been addressed?
If any answer is no, the responsible action is to hold the build or limit it to a controlled, clearly labeled prototype activity while the missing evidence is obtained. The purpose of validation is not to slow every project; it is to prevent an attractive but unproven AI output from becoming an uncontrolled engineering decision.
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