A demonstration can make an artificial intelligence solution appear convincing. However, answering a few questions correctly does not guarantee that it will perform reliably in a real-world process.
Generative AI evaluation makes it possible to determine whether a solution meets the quality, cost and security requirements of the business.
Why is evaluating AI different?
In traditional software, the same input usually produces the same output. This makes it relatively straightforward to verify whether a function behaves as expected.
Generative AI models work differently. They may produce different answers to similar questions and generate plausible-sounding content that contains incorrect information. In addition, their quality depends on the context, the available data, how the question is phrased, the model being used and the configuration of the solution.
For this reason, it is not enough to verify that the application works from a technical perspective. It is also necessary to assess whether its responses are useful, relevant, accurate and appropriate for the process in which they will be used.
Start with the business objective
Before selecting metrics, the company must define what it expects to achieve. An internal assistant may aim to reduce the time needed to find information. A customer service solution may seek to decrease repetitive enquiries. A document management system may help classify contracts or extract specific data.
Each use case requires different criteria. The question should not simply be “Does it answer correctly?”, but also:
- Does it solve the need it was designed for?
- Does it reduce time or operational workload?
- What would be the consequences of an incorrect response?
- When should a person intervene?
- What level of error is acceptable?
Without these definitions, the evaluation may produce a large number of metrics but very little useful information for decision-making.
Create representative test cases
A solution should not be evaluated only with simple examples prepared by the team itself. It is necessary to build a test set based on real situations: frequently asked questions, common documents, ambiguous cases, spelling mistakes, incomplete requests and exceptional situations.
It is also advisable to include cases in which the solution should recognise that it does not have enough information to provide an answer.
The test set should represent the variety of users and contexts the system will encounter. If only the easiest situations are evaluated, the perceived quality will be higher than the actual performance in production.
What should be measured?
Accuracy
The response must contain truthful information that is consistent with the available data. In a solution connected to internal documentation, it is also necessary to verify that it does not invent information that is not present in the sources.
Relevance
A response may be accurate but still fail to answer the actual question. The evaluation should verify whether it addresses the requested topic, uses the correct context and avoids unnecessary information.
Completeness
In some processes, providing only part of the information is not enough. The system must include all the elements required for the response to be useful.
For example, when summarising an incident, it may need to mention the cause, impact, priority and proposed actions.
Security
The solution must comply with permissions, privacy requirements and internal policies. It is also advisable to evaluate how it responds to manipulative requests, out-of-scope questions or attempts to access restricted information.
Cost and speed
The most powerful model is not always the best option. The company should understand the cost per query, document or process, as well as the time required to generate a response.
Quality must be assessed alongside these variables. A minor improvement may not justify a significant increase in cost or latency.
How can hallucinations be evaluated?
Hallucinations occur when the system generates incorrect information or claims that are not supported by the available sources, even though they may be presented convincingly.
It is not enough to ask whether the solution “hallucinates”, because the level of risk depends on the context.
In a creative idea generator, an inaccurate response may have limited impact. In a process related to contracts, healthcare, finance or compliance, the level of tolerance should be much lower.
The evaluation should measure how often unverifiable claims appear, in which types of queries they occur and what consequences they could have.
It should also verify whether the system cites its sources correctly and recognises when it is unable to answer.
Evaluating RAG systems and AI agents
In a RAG solution, the model consults a document repository before generating a response. The evaluation should separate two issues: whether the system retrieved the correct information and whether the model used that information appropriately.
An incorrect answer may be caused by the retrieval of irrelevant documents rather than by the model itself.
Evaluating AI agents is more complex because the system may make decisions, use tools or perform several actions. In this case, it is necessary to assess not only the final result but also the process followed: which decision it made, which tools it used, whether it respected the defined limits and how it responded to an error.
The importance of human review
Not every result requires human review, but the company should define which cases require intervention.
Human review may be appropriate when confidence is low, the decision has a significant impact or the system detects an unusual situation.
The objective is not to review everything manually, but to design controls that are proportional to the level of risk.
It is also advisable to make it easy for users to report incorrect or unhelpful responses. This feedback may reveal problems that were not identified during the initial testing stage.
Evaluation must continue in production
Data, users and models change over time. A solution that performs well today may lose quality after an update or when new documents are added. Evaluation should therefore not end when the solution is launched.
It is necessary to monitor errors, costs, response times, user satisfaction, unresolved queries and variations in quality.
The test set should be repeated periodically and expanded with real cases observed during everyday use.
Generative AI evaluation makes it possible to replace initial impressions with objective criteria related to quality, cost, security and business value.
At MyTaskPanel Consulting, we design AI solutions with testing and monitoring mechanisms adapted to the risks and objectives of each organisation. Do you need to determine whether an AI solution is ready for real-world use? Contact MyTaskPanel Consulting and we will help you evaluate it.
