Workforce Guide Outlines Structured Approach to Testing AI Outputs

A new workforce learning resource has been released that provides a structured method for evaluating the outputs of artificial intelligence applications. The guide focuses on testing AI-generated text for accuracy, safety, grounding, completeness and appropriate behaviour. It aims to help professionals establish consistent evaluation criteria and build practical test matrices for their AI systems.

The resource draws on voluntary guidelines from IMDA's Starter Kit for Testing LLM-Based Applications. It outlines a process that includes defining evaluation criteria, constructing a test matrix, assessing an entire AI application, and retesting after any changes are made. The guide identifies five key risks that testing should address: hallucination and inaccuracy, bias in decision making, undesirable content, data leakage, and vulnerability to adversarial prompts.

Why Structured Testing Matters for AI Applications

AI language models are increasingly used in workplaces to generate text, answer questions and assist with decision-making. These systems can produce fluent and convincing responses, but they can also generate incorrect or harmful content. Without systematic evaluation, it is difficult to know whether an AI application is behaving as intended or introducing errors.

Hallucination, where the model invents facts or sources, remains a persistent challenge. Bias can creep into outputs based on the data the model was trained on. Undesirable content may include offensive language or unsafe advice. Data leakage occurs when the model inadvertently reveals private or proprietary information. Adversarial prompts, designed to trick the system, can cause it to bypass its safeguards. A structured testing approach helps organisations identify these issues before deploying an AI application widely.

Many organisations now recognise that testing AI outputs is not a one-time task but an ongoing process. As models are updated or fine-tuned, their behaviour can change in unexpected ways. A systematic method for evaluation allows teams to catch regressions and maintain confidence in the system over time.

How a Structured Testing Process Works

The guide recommends a process that begins with defining clear evaluation criteria. These criteria should reflect the specific requirements of the application and the context in which it will be used. For example, a customer support chatbot may need to prioritise accuracy and safety, while a creative writing assistant may place more emphasis on tone and style.

Once criteria are set, the next step is to build a test matrix. This matrix lists the types of inputs the application is likely to encounter and the expected correct outputs for each case. The guide suggests testing a variety of scenarios: normal requests, missing information, contradictory details, sensitive inputs, out-of-scope questions and repeated cases. Testing across these categories helps ensure the application handles both routine and edge cases appropriately.

After the test matrix is built, the application is assessed against it. The results are compared against the predefined criteria to determine whether the system passes or fails. The guide emphasises the importance of setting a passing threshold before testing begins. This practice prevents the team from shifting the goalposts after seeing the results, which can obscure genuine problems.

If the application fails any tests, the team can make changes and retest. This cycle of test, adjust and retest is central to improving AI output reliability. The guide also advises checking the documents used by an application, not just the generated text, because the quality of the source material directly affects the quality of the output.

An effective AI output testing process also requires documenting what was tested, what was found and what changes were made. This documentation serves as a record for internal audits and helps build institutional knowledge about how the system behaves.

Common Risks Addressed in the Guide

The five risks outlined in the guide represent the most common failure modes for LLM-based applications. Hallucination and inaccuracy occur when the model generates plausible-sounding but false information. This is particularly dangerous in domains like healthcare, finance or legal advice, where incorrect outputs can have serious consequences.

Bias in decision making arises when the model reflects stereotypes or prejudices present in its training data. This can lead to unfair treatment of certain groups in automated hiring, lending or customer service systems. Undesirable content includes offensive language, hate speech or unsafe instructions that the model should not produce.

Data leakage happens when the model inadvertently reveals information from its training data or from user interactions. This can violate privacy regulations and expose sensitive business information. Vulnerability to adversarial prompts means that a malicious user can craft inputs that cause the model to bypass its safety features, potentially generating harmful content.

A structured testing approach helps organisations systematically check for these risks. By defining what constitutes acceptable behaviour and testing against that standard, teams can reduce the likelihood of deploying an AI application that causes harm or damage.

Building a Test Matrix for Real-World Use

The guide recommends that test matrices include inputs that reflect the full range of expected user behaviour. Normal requests are straightforward questions or commands that the system should handle easily. Missing information inputs test how the system responds when a user does not provide all necessary details. Contradictory details test whether the system can handle conflicting information within a single request.

Sensitive inputs include personal data, health information or topics that require careful handling. Out-of-scope questions test whether the system can recognise when a query falls outside its intended domain and respond appropriately without generating incorrect content. Repeated cases test whether the system produces consistent outputs when given the same input multiple times, which is important for reliability.

Testing across these categories gives a more complete picture of the application's capabilities and limitations. It also helps identify areas where additional training data or fine-tuning may be needed.

Retesting After Changes

AI applications are rarely static. They are updated with new data, fine-tuned for specific tasks, or integrated with other systems. Each change can alter the behaviour of the outputs. The guide stresses the importance of retesting after any modification to ensure that improvements do not introduce new problems.

Retesting should use the same test matrix and evaluation criteria as the original assessment. This allows the team to compare results over time and track whether the system is improving or degrading. If the application fails tests it previously passed, the team can investigate the cause and make adjustments.

This iterative process of testing and retesting is a core part of responsible AI deployment. It acknowledges that AI systems are not perfect and require ongoing oversight to remain safe and effective.

As organisations continue to adopt AI tools for a wide range of tasks, the need for reliable evaluation methods grows. The new resource provides a practical framework that any team can adapt to its own needs. By focusing on defined criteria, structured testing and systematic retesting, the guide helps turn AI output testing from an ad hoc activity into a repeatable process. This approach gives organisations greater confidence that their AI applications will behave as expected, reducing the risk of costly or harmful errors.