Actionable ticket

How can you turn a vague report into an actionable ticket without inventing missing information?

1250 € per session, excl. VAT

In short

This half-day session of 3 hours introduces the components of a technical ticket and provides practice in writing them with generative AI. Working from a fictitious incident, participants distinguish facts from hypotheses, describe how to reproduce the issue, then assess their ticket using a checklist before passing it on.

Why this training?

A report such as ‘the export does not work’ forces the technical team to establish the context before investigating. Describing the action taken, the observed result and the expected result makes it possible to distinguish a defect from a misunderstanding or a change request. AI can help organise notes and identify unresolved questions, but it can also suggest a plausible cause without evidence. Learning therefore focuses as much on writing as on checking added information and explicitly stating what remains unknown. Case study used throughout: At Velsarine Distribution, a fictitious organisation, an administrator reports that exporting orders produces an empty file after a filter is applied. The training pack provides a user message, an anonymised screenshot and a few observations, but deliberately leaves the environment details incomplete. Transfer to the workplace: When handling a future report, reuse the template in the authorised tool, verify each fact with the person concerned and apply the checklist before passing the ticket on. Compare incomplete sections before and after review, without leaving the priority decision to AI.

Objectives of this training

• Identify the sections needed to investigate an incident.
• Distinguish observations, hypotheses and missing information.
• Describe numbered, verifiable steps to reproduce the incident.
• Formulate a simple AI prompt to structure notes without adding facts.
• Assess and then correct a ticket using an explicit checklist.

Who is it for and what are the prerequisites?

Roles and tasks concerned

• Support staff who pass defects on to technical teams.
• Members of product or testing teams who are new to formally documenting incidents.
• Key users who need to describe application malfunctions.

Prior skills

• No prior AI knowledge required.
• Ability to describe an action performed in an application; no programming skills needed.

What you will work on

1. Read a report like an investigator

   Objective: Identify what makes an incident understandable. Differences between a defect, a support request and a change request; the role of the title, context and user impact.

2. Describe before diagnosing

   Objective: Build a verifiable description without assuming the cause. Environment, preconditions, numbered steps, expected and observed results; known frequency and limitations on reproduction.

3. Discover writing assistance

   Objective: Use a simple prompt and check the resulting suggestion. Guided demonstration: Provide the notes, specify the required sections, prohibit factual additions and ask for unknowns to be flagged.

4. Review before passing on

   Objective: Measure writing quality and resolve remaining ambiguities. Peer review, checking attachments, distinguishing between observed impact and priority yet to be decided.

What you take away

• A finalised defect ticket with unknowns flagged.
• A reusable incident description template.
• An AI prompt for structuring information without inventing anything.
• A quality checklist completed before and after correction.

Method and assessment

• Short 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, in accordance with internal terms of use. Prepare the fictitious training pack, a blank template and the checklist; exclude identifiers, secrets and personal data.

Observable criteria: The title specifies the affected function and the symptom, without an unproven diagnosis. ; The steps indicate the known preconditions, the actions and the point at which the problem occurs. ; The observed result and the expected result appear in two separate sections. ; Every detail comes from the training pack; missing information is marked ‘to be confirmed’. ; The checklist counts the criteria met before and after correction, with reasons for the differences..

Frequently asked questions

Do you need to know how to code?

No. The exercises focus on user actions and observations, not code analysis.

Can you use a real incident?

Only after anonymisation and in accordance with internal rules; the fictitious training pack is sufficient for all exercises.

Does AI determine the cause of the problem?

No. A suggested cause remains a hypothesis and replaces neither investigation nor evidence.

Is the ticket created in tracking software?

The session produces transferable content; no configuration or software integration is required.