Making generated visuals more reliable
How do you decide, with supporting evidence, whether a generated visual can be published, corrected or rejected?
3500 € per session, excl. VAT
In short
Two days to prototype a process for checking generated images: build a defect assessment grid, set decision thresholds, organise human review and test its reproducibility. The work results in an illustrated reference framework and a pilot plan, with quality measures and legal safeguards.
Why this training?
An appealing image can distort the product depicted: its proportions, materials, lettering or accessories. Without explicit rules, reviewers confuse aesthetic preference with a defect that prevents approval, allowing misleading representations to pass. Generative AI can help formulate criteria and document anomalies, but does not constitute proof of compliance. A product reference, human review and traceable decisions remain essential. Running case study: The fictitious organisation Avelune Décoration is preparing a lighting catalogue using generated images. Some visuals show impossible cables, inaccurate colours or accessories that are not part of the product. The team must ensure its selection is reliable before publication. Transfer to the workplace: After the training, test the grid on a limited, authorised batch, compare the decisions of two reviewers and revise the thresholds before extending the process.
Objectives of this training
- Break down visual quality into observable criteria
- Build a grid with severity levels and thresholds
- Prototype a documented human validation workflow
- Measure disagreements and missed defects that prevent approval
- Formalise a pilot plan with stopping conditions
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Studio managers, art direction and visual content production
- Designated contacts for quality, communications and publication approval
Prior skills
- Have already used generative AI to prepare visual content.
- Be familiar with the publication constraints and product references relevant to your work.
What you will work on
Define the scope of use
Objective: Define the scope of pre-publication checks. Channels, product references, consequences of errors.
Classify defects
Objective: Separate aesthetics from fidelity. Anatomy, geometry, text, colour, resolution, consistency.
Examine permissions
Objective: Identify barriers to distribution. Rights, consent, trademarks, applicable notices, deepfakes and provenance.
Draft the grid
Objective: Make decisions reproducible. Severity, tolerances, defects that prevent approval; limitations of averages.
Calibrate reviewers
Objective: Reduce divergent interpretations. Independent double review and documented adjudication.
Assemble the prototype
Objective: Make every visual decision traceable. Identifier, version, evidence, status, person responsible.
Prepare the test
Objective: Avoid a biased test. Held-out batch, adjudicated reference, diversity of defects.
Conduct the review
Objective: Test the complete workflow. Acceptance, correction, rejection and escalation.
Measure errors
Objective: Characterise the limitations of the prototype. Agreement between reviewers, false rejections, missed defects that prevent approval.
Plan the pilot
Objective: Set a framework for a limited trial. Responsibilities, sampling, stopping, revalidation after modification.
What you take away
- Illustrated reference framework for defects and decision thresholds
- Prototype control form and validation workflow
- Test report covering errors and disagreements
- Pilot plan: scope, responsibilities, indicators and stopping
Method and assessment
- Short teaching inputs followed by immediate practice on the running case study.
- Individual work followed by work in pairs, with participants reviewing each other’s outputs.
- Guided group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: Prepare fictitious or anonymised images, accompanied by product references and known defects. Check access to a tool authorised by your organisation and the rules for transmitting images.
Observable criteria: Each criterion includes a verifiable observation and an example. ; Each blocking defect results in rejection or explicit escalation. ; Decisions retain the reference, rationale, version and reviewer. ; Errors and agreement are calculated on the reserved batch. ; Rights, consents and notices are checked separately..
Frequently asked questions
Can AI approve images on its own?
Not in this prototype: the decision rests with an identified reviewer.
Do you need programming skills?
No, a structured assessment grid and register are sufficient to test the workflow.
Can we use our own visuals?
Only if they are anonymised and authorised; otherwise, the exercises use fictitious cases.
Will the process be rolled out at production scale?
No. The 14 hours, excluding breaks, produce a tested prototype and a pilot plan.
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