Checking AI outputs

How do you decide whether an output from generative AI is reliable enough to use in your profession?

1960 € per session, excl. VAT

In short

This 7-hour day, excluding breaks, enables you to develop a method for checking generative AI outputs within a business process. You define verifiable criteria, check responses against sources, compare assessments and document a decision to accept, correct or reject in an operational dossier.

Why this training?

A fluently written response may contain a contractual error or a promise that cannot be kept. Without explicit criteria, reviews often prioritise style and overlook flaws that change the business decision. A shared assessment framework makes judgements explainable. Checking against reference documents and comparing assessors’ evaluations makes it possible to distinguish a writing preference from a non-conformity that prevents acceptance, then target corrections. Running case study: The fictitious organisation Avelune Équipements is preparing responses to complaints from business customers. Using fictitious orders, contractual terms and correspondence, participants check several generated responses: some invent a timeframe, omit a qualification or propose an unauthorised commitment. Transfer to the workplace: Apply the framework to the next batch of permitted business outputs, have disputed cases reviewed and monitor the acceptance rate, critical errors and reasons for reworking. Adjust the criteria without concealing the flaws identified.

Objectives of this training

  • Define observable criteria suited to the business risk.
  • Build a weighted assessment framework with flaws that prevent acceptance.
  • Check claims and calculations against sources.
  • Compare two assessments and explain their differences.
  • Measure quality before and after correction.
  • Document a decision to release, rework or reject.

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Business managers who validate documents produced using generative AI.
  • Quality leads and regular users responsible for checking responses or syntheses.

Prior skills

  • Be able to use a generative AI assistant and formulate a structured instruction.
  • Know the rules for validating a deliverable in your field.
  • Be able to calculate an average and a percentage in a table.

What you will work on

  1. Define the risks of release

    Objective: Define what makes a response usable. Intended use, recipient, severity of errors, writing quality and compliance with business requirements.

  2. Build the decision framework

    Objective: Turn expectations into measurable criteria. Accuracy, completeness, relevance, traceability; anchored scale, weighting and thresholds.

  3. Check responses against evidence

    Objective: Detect unsupported claims. Non-existent references, contradictions, omissions, calculations and the distinction between facts and assumptions.

  4. Calibrate assessors

    Objective: Reduce differences in scoring. Independent scoring, presentation bias, adjudication; limitations of AI self-assessment.

  5. Correct and measure differences

    Objective: Verify the actual effect of corrections. Targeted correction, regression checks and comparison using unchanged criteria.

  6. Formalise business validation

    Objective: Make the checks reproducible. Responsibilities, retention of evidence, escalation and monitoring of reasons for rejection.

What you take away

  • Weighted assessment framework and rules for preventing acceptance.
  • Verification log with references and anomalies.
  • Comparison table of assessments and corrections.
  • Validation dossier with a decision and a designated person responsible.

Method and assessment

  • Short teaching inputs followed by immediate practice using 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: Choose a type of business output and identify three errors considered unacceptable. Arrange access to a tool authorised by the organisation; use only fictitious or anonymised cases.

Observable criteria: Each criterion includes a scale and an example of a flaw. ; Each sensitive claim is linked to a source or flagged as unverifiable. ; A blocking flaw prevents acceptance despite a high average. ; Disagreements between assessors are documented and adjudicated. ; The final decision is based on the checks and the before-and-after comparison..

Frequently asked questions

Is a good average enough?

No: a contractual error that prevents acceptance requires reworking or rejection.

Can AI carry out the checks on its own?

AI can assist with reviewing, but it replaces neither evidence nor human validation.

Can business documents be used?

Yes, only when anonymised and used in tools authorised by the organisation.

Is the framework suitable for a synthesis?

Yes, after adapting the criteria, particularly faithfulness to sources and coverage of essential information.

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