Verifiable quality checks

How can you turn goods receipt requirements into simple, observable and traceable checkpoints with the help of AI?

1250 € per session, excl. VAT

In short

This half-day session of 3 hours offers a guided introduction to generative AI for formalising quality checks. Starting with a fictitious receipt of supplies, participants draft a checklist, verify its criteria and test it against several anomalies before preparing to adapt it to their own context.

Why this training?

An instruction such as ‘check the parcel’ leaves each operator to interpret what they should observe. A useful checklist specifies the item being checked, the expected reference, the evidence to record and the action to take when a discrepancy arises. AI can suggest an initial structure and rephrase checks that are too vague. It can also invent tolerances or overlook a requirement: the training teaches participants to check each suggestion against the documents provided, without delegating the conformity decision to AI. Case study used throughout: The fictitious organisation Tervaline Fournitures receives boxes of document wallets. Quantity discrepancies, incorrect references and damaged packaging complicate the checks. Participants have an order, a delivery note and descriptions of fictitious parcels to build and then test a goods receipt checklist. Application at work: After the session, choose a routine goods receipt and adapt the checklist using internal requirements authorised for use. Have the quality manager review the criteria before use, then record any ambiguities encountered during an initial supervised trial.

Objectives of this training

  • Identify verifiable requirements in an order and a delivery note
  • Distinguish a factual observation from a subjective judgement
  • Formulate an AI prompt to structure checkpoints
  • Write criteria linked to evidence and a status
  • Test the checklist and identify the corrections needed

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Operations staff responsible for goods receipts or simple checks
  • Purchasing and supply assistants who monitor supplier discrepancies
  • Quality coordinators wishing to explore drafting support

Prior skills

  • No prior AI knowledge required: the initial instructions are explained and carried out step by step.
  • Ability to read a goods receipt document and complete a simple table.

What you will work on

  1. Define what needs to be checked

    Objective: Select the relevant requirements for goods receipt. Difference between a requirement, a check and evidence; limitations of a checklist; AI’s supporting role.

  2. Request an initial structure

    Objective: Obtain a table without allowing AI to invent the rules. Prompt including context, documents, expected columns and a request to flag missing information.

  3. Make checks actionable

    Objective: Replace vague wording with verifiable observations. One check per row; expected criterion, evidence, status, person responsible and planned handling of discrepancies.

  4. Test the checklist against discrepancies

    Objective: Assess the document’s coverage and clarity. Test using a compliant case and a case containing anomalies; comparison with the model answer; human validation.

What you take away

  • Goods receipt inspection scope sheet
  • Reusable AI prompt with explicit limits
  • Goods receipt checklist for completion and corrected version
  • Test grid with detected anomalies and corrections

Method and assessment

  • Brief explanations followed by immediate practice using the ongoing case study.
  • Individual work followed by work in pairs, with peer review of the outputs.
  • Guided group discussion: what works, what needs correcting and what remains a human decision.
  • Preparation: Arrange access to a generative AI assistant authorised by the organisation; no particular commercial tool is required. Prepare the fictitious file provided; any additional example must be anonymised and authorised before entry.

Observable criteria: Each requirement in the file appears in a check or in a justified exclusion. ; Each row describes an observation and a criterion without an invented tolerance. ; The statuses compliant, non-compliant and not applicable are distinguished; any inability to carry out a check is flagged. ; Each discrepancy identified includes evidence and an action consistent with the scenario provided. ; The report measures detected anomalies against the anomalies present and lists the remaining ambiguities..

Frequently asked questions

Do you need prior knowledge of AI?

No. Drafting the first prompt is guided using the goods receipt documents.

Does AI define quality tolerances?

No. They must come from validated requirements; missing information remains to be confirmed.

Can you use actual supplier documents?

Only if they are anonymised and their use in the tool is authorised by the organisation.

Does the checklist replace a quality procedure?

No. It facilitates the execution of checks and must be validated in accordance with internal rules before use.

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