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An AI-generated drawing, CAD model, calculation, or specification is a proposed design—not evidence that the design is fit to build. Validate it by connecting a controlled design baseline to measurable requirements, suitable analyses, inspections, demonstrations, and tests performed in the intended operating context. Verification shows that the product meets specified requirements; validation shows that it is the right product for its users and mission.
This is general systems-engineering guidance. The applicable engineering discipline, jurisdiction, hazard level, materials, loads, and consequences determine which codes, licensed professionals, hazard analyses, and regulatory approvals are required.
Validation is more than checking whether the AI followed the drawing
NASA summarizes the distinction this way: “While verification proves whether ‘the product was done right,’ validation proves whether ‘the right product was done.’” The distinction appears in NASA’s Systems Engineering Handbook, section 5.0 Product Realization.
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Verification compares the design or built item with its documented requirements. It asks questions such as:
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- Are the dimensions, performance limits, interfaces, and materials what the specification requires?
- Were calculations performed with the stated units, loads, boundary conditions, and acceptance limits?
- Does the manufactured item match the released drawing or model?
Validation asks whether those requirements describe the real need and whether the item works for its stakeholders in its operating environment. NASA links validation to stakeholder expectations and the concept of operations, with evaluation under realistic or simulated conditions. A component can pass every dimensional check and still be unusable, inaccessible, incompatible with adjacent equipment, or unsafe in the environment where people must operate it.
AI makes this distinction especially important. A model can produce a coherent-looking geometry and fluent explanation while silently assuming a different material, load case, unit system, manufacturing process, or user task. Treat the output as a hypothesis until engineers establish the requirements and objective evidence for both verification and validation.
Define intended use and constraints before reviewing the generated design
Do not begin by asking whether the AI output “looks right.” First describe what the system must accomplish, who depends on it, and what limits apply. NASA’s fundamentals guidance frames the central question as, “Are we building the right product for our users and other stakeholders?” See NASA’s SEH 2.0 Fundamentals of Systems Engineering.
Identify the operational context
Write a concise concept of operations (ConOps) that covers the complete life cycle:
- Users, operators, maintainers, installers, bystanders, and other affected stakeholders.
- Tasks the design must support, including startup, normal operation, adjustment, cleaning, repair, transport, storage, and disposal.
- Where it will operate: indoor or outdoor locations, altitude, temperature, humidity, vibration, contamination, electromagnetic conditions, and access restrictions as applicable to the discipline.
- Interfaces with people, structures, software, power, fluids, communication networks, tooling, and neighboring components.
- Foreseeable misuse, single-point failures, recovery actions, and maintenance errors.
- Things the design must not do, such as exceed a force, temperature, emission, exposure, or access limit.
Invite representative users or operators to review the ConOps. Their feedback can expose an incorrect assumption before it becomes an expensive change.
Turn expectations into measurable requirements
Each requirement needs an observable pass/fail condition. Avoid vague statements such as “robust,” “easy to use,” or “high performance” unless you define how the team will measure them.
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| Requirement area | Questions to answer | Typical acceptance evidence |
|---|---|---|
| Function and performance | What must the item do, at what capacity, speed, accuracy, response time, or duty cycle? | Analysis, demonstration, or test with recorded results and limits |
| Environment and life cycle | Which operating, storage, transport, and abnormal conditions must it tolerate? | Environmental analysis, inspection, or representative testing |
| Interfaces | What must align, connect, communicate, or exchange loads and clearances? | Interface inspection, fit check, interoperability demonstration, or test |
| Safety and compliance | Which hazards, codes, standards, regulations, and professional approvals apply? | Hazard analysis, calculations, inspection, certification, and authority review as required |
| Human factors | Can intended users understand, reach, control, service, and recover from foreseeable errors? | Demonstration or user evaluation in a representative task |
| Manufacture and maintenance | Can the design be produced, inspected, repaired, and retired with available processes and tools? | Process review, prototype inspection, demonstration, and production evidence |
Separate mandatory requirements from preferences. Record who owns each requirement and how a disagreement will be resolved. If stakeholders want incompatible outcomes, resolve the conflict before releasing a build package.
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Validation results are meaningful only when everyone knows exactly which design was evaluated. NASA describes configuration control and a verified baseline as protection against costly or problematic changes; its product-realization guidance is available at NASA’s Product Realization reference.
- Inventory the generated material. Collect native CAD files, exports, drawings, calculations, scripts, specifications, images, prompts or input data that materially influenced the output, and any AI-generated explanatory text.
- Assign a revision. Give the reviewed package a unique identifier and revision status. Record the software versions, plug-ins, solver settings, and file formats used to open or calculate it.
- Record assumptions. State units, coordinate systems, datums, materials and their property sources, manufacturing processes, loads, constraints, safety factors, tolerances, environmental conditions, and simplifications.
- Capture interfaces. Link the design to surrounding components, electrical or software interfaces, mounting points, envelopes, utilities, and human access zones.
- Lock the evidence set. Link each analysis, inspection record, demonstration script, and test report to the exact revision. Store raw measurements as well as summarized results.
- Control changes. Require a change description, impact assessment, reviewer decision, and new revision whenever geometry, material, process, requirement, or analysis input changes.
Do not silently “clean up” an AI model while reviewing it. A change that appears cosmetic can alter mass, clearances, stress concentrations, airflow, thermal behavior, or an interface. Make the change, document it, and reassess affected evidence.
Trace every requirement to an appropriate form of evidence
NASA identifies four common verification methods—analysis, inspection, demonstration, and test—and notes that the suitable method depends on the product’s position in the system and its life-cycle phase. The definitions and completion criteria are in the NASA Systems Engineering Handbook appendix.
Build a requirement-to-evidence matrix before building hardware. A useful matrix records the requirement, responsible owner, method, procedure, configuration, acceptance rule, result, anomalies, and approval.
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| Method | What it can establish | Important limitations | Evidence to retain |
|---|---|---|---|
| Analysis | Predicted performance, compatibility, capacity, or behavior from mathematical models and stated inputs | Results depend on model fidelity, input quality, boundary conditions, and assumptions; it may not reveal an unmodeled failure mode | Model version, inputs, equations or solver settings, assumptions, sensitivity or uncertainty treatment, and review record |
| Inspection | Observable physical or documentary features such as dimensions, markings, workmanship, material certificates, or configuration | It cannot establish behavior that is not directly observable during the inspection | Procedure, calibrated equipment where required, measured values, photographs or records, inspector, date, and disposition |
| Demonstration | Whether a user or operator can perform a defined task or whether an interface behaves as specified in a controlled scenario | Results may not represent all loads, environments, endurance periods, or abnormal conditions | Scenario, participants, setup, observed steps, deviations, acceptance decision, and recording |
| Test | Measured behavior of a prototype or final product under specified conditions | Tests cost time and equipment; a narrow test does not establish untested conditions or a different configuration | Test plan, instrumentation, calibration status, raw data, environmental conditions, anomalies, analysis, and approval |
Choose among methods using six questions: Which requirement or stakeholder expectation does the method address? How directly does it represent real use? Are inputs and measurements traceable? What uncertainty and model limitations remain? What is the consequence if the method misses a defect? What are the cost, schedule, equipment, and representative-user constraints?
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Audit the AI output, inputs, and assumptions
AI-specific review starts with ordinary engineering discipline and adds provenance checks. Reviewers should be able to explain every consequential feature without relying on the model’s confidence or fluent narrative.
Check provenance and completeness
- Identify which portions were generated, edited by a person, imported from a library, or copied from an earlier design.
- Preserve the prompt or input data when it affects geometry, calculations, requirements, or material selection.
- Check whether the model invented standards, citations, material properties, tolerances, or supplier capabilities. Verify each against the authoritative specification or manufacturer data used by your organization.
- Look for omitted requirements. AI commonly optimizes the explicit request while ignoring maintenance access, transport, disposal, cybersecurity, accessibility, or failure recovery unless those concerns are stated.
Recalculate fundamentals independently
Recompute critical quantities with an independent method or reviewer. Check unit conversions, sign conventions, coordinate systems, datum references, load paths, boundary conditions, contact assumptions, material properties, temperature dependence, fatigue or life assumptions where applicable, and the distinction between nominal and worst-case values. Confirm that the stated manufacturing process can achieve the geometry and tolerances and that inspection access exists.
Review interfaces and failure behavior
Inspect every interface rather than only the AI-generated part in isolation. Confirm fit, alignment, fasteners, connectors, power or fluid capacity, communication protocols, software assumptions, and service clearances. Ask what happens when an input is missing, a component is out of tolerance, power is interrupted, a sensor disagrees, an operator makes a foreseeable mistake, or a single element fails. Escalate hazards that require specialist methods instead of treating a generic model review as sufficient.
Use independent human judgment
A qualified engineer or other competent authority should review requirements, assumptions, calculations, and acceptance decisions appropriate to the discipline and jurisdiction. Independence can mean a separate person, method, or tool; the aim is to avoid merely approving the same AI reasoning that produced the design.
Use simulation as evidence, not as a safety certificate
NASA describes modelling and simulation as analytical tools and testing as a way to obtain detailed behavioral data at specified points. Simulation can expose an overloaded feature, an interference, an unstable control response, or an unfavorable thermal condition before material is committed. It can also be the most practical evidence when a prototype is unavailable.
A simulation does not by itself prove real-world safety. Before accepting its output, document:
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- Whether the model represents the released geometry, materials, interfaces, and operating state.
- Whether loads, restraints, contacts, environmental conditions, and initial conditions match the requirement and the intended use.
- Which features were simplified, omitted, or treated as ideal, and how those simplifications could affect the decision.
- Whether input data are traceable, current, and appropriate for the material, process, temperature, and manufacturing variation.
- How numerical error, measurement uncertainty, parameter variation, and model-form uncertainty affect the margin to the acceptance limit.
- What independent checks, benchmark cases, or physical observations support the model’s implementation and applicability.
Use the result to decide what must be inspected or tested next. When consequences are high, uncertainty is large, margins are small, or the model omits important physics, obtain representative physical tests and specialist review rather than stretching the simulation beyond what it can establish.
Inspect prototypes and plan proportionate tests
Inspection is appropriate for observable features; testing is appropriate for behavior that analysis or inspection cannot establish adequately. NASA notes that tests can use prototypes as well as final products. The prototype should represent the production configuration closely enough for the question being asked, or the differences must be documented and justified.
Start with dimensional and configuration checks
Compare the physical item with the released drawing and interface control information. Check critical dimensions, orientation, hole or connector locations, clearances, surface or marking requirements, material identity, and workmanship. A digital caliper can be a practical example for a dimensional check against a drawing, but it is not a universal instrument and cannot replace tolerance analysis, material verification, functional testing, or safety review. Use equipment with the accuracy, range, calibration status, and resolution appropriate to the requirement.
Write a test plan before operating the prototype
- State the requirement and the question the test must answer.
- Define the configuration, setup, fixtures, instrumentation, sampling, environmental conditions, and operator actions.
- Specify the acceptance limit, uncertainty treatment, stopping rules, and actions for an unsafe or anomalous result.
- Record calibration status, raw data, observations, photographs or logs, and deviations from the plan.
- Have a competent reviewer evaluate the result and decide whether the requirement is met, partially met, failed, or not demonstrated.
Do not convert a convenient demonstration into a claim about endurance, extreme conditions, abuse, or production consistency. Those claims require evidence that represents the relevant conditions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Validate with users in the intended context
Validation should reproduce the circumstances that matter to stakeholders, using realistic or deliberately representative conditions. NASA’s process guidance calls for planned validation, analysis and reporting of results, preservation of work products, and user or operator participation whenever possible; see NASA’s product-realization process.
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- Use the intended workflow, tools, access restrictions, protective equipment, and information displays.
- Include representative operators and maintainers, not only the design team.
- Exercise normal operation, startup and shutdown, maintenance, recovery, and foreseeable misuse within safe limits.
- Observe workload, reach, visibility, sequence errors, ambiguous indicators, and workarounds that users create.
- Run environmental or interface conditions that the ConOps identifies as significant.
- Record what users expected to happen, what actually happened, and whether the difference creates a requirement change.
A successful user demonstration does not waive code compliance, structural analysis, cybersecurity controls, or regulatory approval. It answers the user-and-context part of validation and must be combined with the other evidence required for the design.
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Resolve anomalies under configuration control
Any failed check, unexplained measurement, unexpected behavior, or disagreement between analysis and test is an anomaly—not a reason to discard an inconvenient result.
- Contain the issue and identify affected configurations, requirements, users, and hazards.
- Preserve raw data, logs, photographs, model files, and the exact setup before attempting a correction.
- Classify the cause: requirement error, design defect, manufacturing variation, instrumentation problem, model limitation, operator action, or unknown.
- Decide whether to correct the design, clarify the requirement, improve the method, add a control, or obtain specialist review.
- Update the baseline and repeat every analysis, inspection, demonstration, or test whose validity the change could affect.
- Close the anomaly only when objective evidence supports the decision and an authorized reviewer records approval or an explicitly accepted deviation.
Retain the requirement matrix, procedures, raw and summarized results, assumptions, review comments, deviations, anomaly records, and final approval. This record lets a later team understand not just the final design, but why it was accepted.
Use AI governance guidance without treating it as product approval
NIST’s AI Risk Management Framework (AI RMF) is a voluntary framework for 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. These resources can help an organization assign responsibilities, identify AI-specific risks, and document governance; they do not replace engineering codes, hazard analyses, professional judgment, or regulatory review.
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NIST’s TEVV-Athlon Framework for Evaluating AI Systems describes a customizable, four-stage method that 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. It is an AI-system evaluation resource, not a complete product-safety approval route for every engineering design.
Know when the design is not ready to build
Pause fabrication or deployment when any of these conditions remains unresolved:
- The intended users, operating environment, or acceptance criteria are unclear.
- The reviewed files cannot be tied to a unique revision or the AI-generated inputs are unavailable.
- A critical material property, load, interface, or boundary condition is guessed rather than sourced or measured.
- Simulation margins depend on assumptions that have not been checked, or the model omits a consequential failure mode.
- A prototype differs from the released design in a way that could change the result.
- A test is planned without a safe setup, suitable instrumentation, calibrated equipment, or an objective acceptance rule.
- Users or maintainers identify a task failure that the written requirements do not address.
- A code official, regulator, licensed engineer, safety specialist, or other required authority has not completed the applicable review.
- An anomaly is explained away without preserving data and repeating affected evidence.
Building can proceed only after the responsible engineering authority determines that the remaining uncertainty is understood, controlled, and acceptable for the consequences of failure. For high-consequence systems, independent review and formal authorization may be mandatory even when every internal check passes.
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