Evaluation of generative AI: operate reliably over the long term
By Kevin Kröger, Geschäftsführer, Software und Plattformbetrieb
Evaluation of generative AI leads to comprehensible model decisions based on real specialist cases if monitoring, maintenance, responsibility and restart are clarified before tool selection. The practical start consists of a limited scope of application, designated responsible parties, measurable baseline values and a fallback path. What is important is not the amount of technology used, but whether benefits, risks and operation can be proven together.
What needs to work every month after the launch?
The most common starting point is: Demos seem convincing, but say little about critical special cases and later operation. Before a provider or tool is selected, it must be clear which specific decision is to be improved, which users are affected and which result must be verifiable. For this perspective, the focus is on monitoring, care, responsibility and restart. Note assumptions separately from proven facts and identify points that would preclude starting. This makes offers comparable and prevents an impressive individual demonstration from replacing actual everyday work.
Which problems need to become visible first?
When evaluating generative AI, the main risk is often selection based on gut feeling, changing models without checking and undetected losses in quality. Creates a concise map of process steps, data paths, systems, handoffs and responsible roles. Adds current processing time, error consequences and known exceptions to each step. Conversations with real users are more important than a pure management perspective. The goal is not a hundred-page specification, but rather a common picture of where damage occurs, what limits apply and which small part can be improved first.
What does a reliable solution look like?
A sustainable structure combines versioned test sets, separate quality criteria, red team cases and repeatable comparison runs. Starts with a zoned pilot containing normal and critical cases. Defines in advance who gives technical approval, who is allowed to make technical changes and when the pilot will be stopped or dismantled. Interfaces, data formats and protocols should be designed in such a way that decisions can be traced later. Documents not only the target architecture, but also operation, maintenance and the way out of the solution. This means that the result remains manageable even after the project team has finished.
Which market trends are really relevant?
Automation, platform services and AI shorten development times, but at the same time increase the speed of changes and the number of external dependencies. When evaluating generative AI, it therefore matters less whether an individual trend sounds modern. What is relevant is whether it measurably supports comprehensible model decisions based on real specialist cases and fits into existing responsibilities. Requires transparent versions, open export channels, comprehensible security commitments and regular reassessment. Consciously foregoing is a good decision if additional operating costs or risk exceed the expected benefit.
How are quality, safety and costs checked together?
Measures technical accuracy, reference to sources, reliable rejection and costs per checked answer in representative cases and separately according to normal operation, special cases and disruptions. The cost accounting includes implementation, internal collaboration, licenses, infrastructure, monitoring, maintenance, training, readiness and subsequent change. Security isn't done with a one-time release: permissions, logs, updates and recovery need fixed review dates. Each key figure is given a starting value, a target and a person who can act if there is a deviation. This turns a technical delivery into a controllable operational process.
What is the next sensible step?
Conducts a ninety-minute working workshop with subject matter experts, quality assurance, development and risk owners. Brings a real process, two problematic special cases, existing contracts and known key figures. The end result is a clear pilot scope, three measurable success criteria, open risks, required data and a responsible next date. Use the checklist in this article to prepare and link the result to the appropriate performance and regionality page. This creates a testable starting point instead of a non-binding collection of ideas.
Evaluation of generative AI: work checklist before the next appointment
- Write down the goal and expected result for evaluating generative AI in one sentence
- Assign responsibility between subject matter experts, quality assurance, development and risk owners by name
- Measure the initial value for technical accuracy, reference to sources, certain rejection and costs per checked answer before the start of the project
- Completely capture data, systems, service providers and technical dependencies
- Document mandatory criteria, reasons for exclusion and accepted residual risks
- Define pilot, acceptance, fallback path and escalation before implementation
- Plan operation, maintenance, testing and budget for at least twelve months
- Check the results with a specialist user after four to eight weeks
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The central statements in this article were reviewed against the following primary sources.
Frequently asked questions
- How big should a first step be when evaluating generative AI?
- Small enough that results, risks and operations can be checked in four to eight weeks, but large enough to map a complete real work route.
- Which people need to be involved from the start?
- At least subject matter experts, quality assurance, development and risk managers. Names and decision-making rights are more important than a long list of only informed bodies.
- When should a project be stopped?
- If must-have criteria are not met, critical risks have no person responsible or the benefits cannot be reliably measured compared to the initial value.
- How do you prevent permanent provider dependency?
- Data export, interfaces, documentation, termination process and replacement operation are evaluated before the contract is concluded and regularly tested in practice.