Specify the data generative AI output quality assurance depends on, where it originates and who is accountable for its quality.
Evaluating and Testing the Quality of Generative Artificial Intelligence Output
A senior-level treatment of generative AI output quality assurance, focused on what changes outcomes.
📍 Cairo🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
The constraint on generative AI output quality assurance is rarely the model or the platform — it is the data and the operating discipline behind it. Boards are asking for measurable returns from quality assurance, not demonstrations. Teams frequently over-invest in documenting this area of digital and data-driven work and under-invest in testing it. The course sets out a working method for generative AI output quality assurance that participants can apply the week they return. Where this aspect of digital and data-driven work is measured, it improves; where it is only discussed, it drifts. The course leaves participants able to diagnose weaknesses in quality assurance before they become incidents. The programme works equally well for those formalising generative AI output quality assurance for the first time and those improving an existing approach. Sessions alternate between guided analysis of quality assurance and supervised application. The closing exercise tests whether the participant's plan for this part of digital and data-driven work survives a hostile question.
Expected Learning Outcomes
Verify that improvements to quality assurance have held six months after they were introduced.
Recognise early indicators that generative AI output quality assurance is drifting away from its intended design.
Review contracts and agreements for the obligations they create around quality assurance.
Establish version control and rollback for every component of generative AI output quality assurance that reaches production.
Prepare the human side of quality assurance: who is retrained, who is redeployed, and when they are told.
Assess whether generative AI output quality assurance should be built in-house, bought, or delivered through a partner.
Who Should Attend
Product owners prioritising the roadmap for generative AI output quality assurance.
Operations managers whose processes are changed by quality assurance.
Business analysts translating requirements for generative AI output quality assurance.
Chief information officers accountable for the investment in quality assurance.
Risk managers assessing the exposure created by generative AI output quality assurance.
Experienced practitioners formalising an approach to quality assurance that has grown up informally.
Course Modules
Generative AI output quality assurance: architecture, integration and the existing estate
2 sessions · 8 pointsSession 1Who owns generative AI output quality assurance once the project team disbands
- Define the service level generative AI output quality assurance must meet and what happens when it is missed.
- Identify the skills the team lacks to operate quality assurance independently.
- Build the competence framework that supports generative AI output quality assurance.
- Agree what will be standardised in quality assurance and what will not.
Session 2Who answers for quality assurance, and to whom
- Decide which legacy process generative AI output quality assurance retires, and set the date.
- Establish who is informed, consulted and accountable in quality assurance.
- Set the metrics that will show whether generative AI output quality assurance is drifting from its intended behaviour.
- Agree the indicators that will show whether quality assurance is improving.
Quality assurance: vendor selection and avoiding lock-in
2 sessions · 8 pointsSession 1Sizing quality assurance honestly before committing budget
- Decide what will be stopped to create capacity for generative AI output quality assurance.
- Map the handovers in quality assurance between functions and secure them.
- Plan how generative AI output quality assurance is versioned and how a bad release is rolled back.
- Specify the fallback path when quality assurance is unavailable.
Session 2Where quality assurance touches systems nobody wants to change
- Confirm that reporting on generative AI output quality assurance reaches the people who can act.
- Test quality assurance against edge cases drawn from real historical records.
- Classify the data in generative AI output quality assurance and apply access controls that match the classification.
- Confirm that contractual obligations around quality assurance are understood.
Quality assurance: from pilot to production
2 sessions · 8 pointsSession 1The governance quality assurance needs and the governance it does not
- Estimate compute and licensing cost for generative AI output quality assurance at expected and at peak load.
- Verify that quality assurance still performs when input volume doubles unexpectedly.
- Test the procedure for generative AI output quality assurance against a realistic scenario.
- Prepare the summary of quality assurance that senior management will read.
Session 2Making generative AI output quality assurance secure without making it unusable
- Agree the smallest change to generative AI output quality assurance that would be visibly useful.
- Design the pilot for quality assurance so that a negative result is still useful.
- Agree who is on call for generative AI output quality assurance outside working hours.
- Measure the current quality of the data feeding quality assurance before assuming it is usable.
Quality assurance: security, privacy and regulatory obligation
2 sessions · 8 pointsSession 1What to measure in quality assurance and what to ignore
- List the data sources generative AI output quality assurance consumes and confirm each has a named owner.
- Rehearse the briefing on quality assurance that would follow an incident.
- Estimate the resource generative AI output quality assurance requires to run as designed.
- Build the user briefing that explains what quality assurance does and does not decide.
Session 2Where quality assurance typically breaks, and why
- Record the rationale for each significant choice made about generative AI output quality assurance.
- Confirm the retention and deletion rules applied to data inside quality assurance.
- Assign responsibility for keeping documentation of generative AI output quality assurance current.
- Arrange the handover of quality assurance so capability survives staff changes.
Choose the package that suits you
Silver Package
At least 3 people
USD1,250
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Gold Package
At least 3 people
USD1,850
- 5-night stay in a 5-star hotel
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
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