Good AI solutions do not hide uncertainty. They expose limits and define who reviews, decides and intervenes.
Errors cannot be prevented by a better prompt alone. Responsible use combines suitable tasks, representative tests, limited permissions, transparent sources and explicit ownership.
why plausible but false answers occur
where systematic bias arises and how it can be tested
the roles of testing, monitoring and human approval
how staff can recognise uncertainty and respond correctly
These questions connect a technical term to a concrete organisational decision.
Which errors are possible, and what would their consequences be?
How are false or unsupported outputs detected before use?
Who reviews, monitors and decides when the system or context changes?
An AI hallucination is a plausible but false output, or one that is not supported by the information available to a generative model.
Evals are structured assessments in which an AI solution is run against representative test cases and scored using predefined criteria. They reveal how reliably it performs a specific task and where its limits lie.
Bias in AI refers to systematic distortions that make results less reliable, useful or fair for particular cases, situations or groups. These distortions can arise across the entire lifecycle – from problem formulation and data collection through the model and thresholds to use, monitoring and feedback in operation.
Prompt injection is an attempt or unintended effect that manipulates a language model through input so it disregards original rules, exposes data or proposes or performs actions outside its intended purpose.
Guardrails are technical and organisational controls that detect, flag, transform or block undesirable content, sensitive data, attacks or disallowed actions. Content filters are one type of guardrail applied to input and output.
Human review is needed when an AI error could have significant impact, is difficult to reverse or affects people's rights and interests. Approval is effective only when the person is informed, authorised and not pushed towards automatic confirmation.
AI monitoring captures and assesses technical performance, result quality, data and tool use, security events and human feedback. Logs provide traceable events without retaining unnecessary sensitive content indefinitely.
In an AI incident, first contain potential impact, then preserve evidence and affected versions, identify causes and affected parties, and restore service only after documented correction and renewed testing.
Shadow AI is the use of AI services, models, browser extensions or integrations without the knowledge, assessment or control of the responsible organisational functions.
AI red teaming is a structured adversarial testing process in which specialists attack or misuse an AI system under controlled conditions to discover security flaws, undesirable behaviour and previously unknown risks.
We translate your starting point into understandable options and a realistic next step.
