Evidence-based answers for enterprises, with practice examples, review criteria, transfer artefacts and named primary sources.
Generative AI creates new content from learned patterns. Enterprises gain dependable value only when task, data, review, approval and accountability form one controlled workflow.
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An LLM processes language as numeric token sequences and predicts likely continuations from its context. It can apply patterns remarkably well, but has neither human understanding nor an inherent truth check.
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A hallucination is an unsupported or false output that may sound plausible. It is not eliminated by more polite prompting; it is controlled through sources, review procedures, task boundaries and accountable approval.
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A context window contains the information a model can consider during one request. More context is not automatically better: relevance, currency, consistency and structure matter more than volume.
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Human in the loop means a qualified person reviews, decides or approves at defined points. A confirmation button without time, information and authority is not an effective control.
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Retrieval-augmented generation combines search and generation. Before answering, relevant content is retrieved from approved sources and supplied as context. This improves currency and verifiability but does not make answers automatically correct.
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A reliable prompt specifies task, context, inputs, expected output, boundaries and evaluation criteria clearly enough for outputs to be assessed reproducibly and improved deliberately.
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A reusable prompt template separates stable instruction from variable inputs and records purpose, owner, version, test status and operating limits. Only then does a good one-off prompt become a maintainable work aid.
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Dependable document analysis separates evidence location, extraction, interpretation and judgement. If these stages are mixed, plausible claims can no longer be traced to the original.
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A controlled AI workflow connects work steps through defined inputs, states, quality checks, exception paths and approvals. Only observable and controllable work should be automated.
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AI quality is task-specific. An output is not generally “good”; it may be accurate, complete, grounded, relevant, consistent, safe, understandable and conformant to a required format.
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Evaluation tests an AI system with representative tasks, expected properties and documented methods. A few demonstration prompts are not dependable evidence.
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AI governance defines who may use which AI for what purpose and data, how risks are assessed, results reviewed, exceptions handled and evidence retained. It should enable use while making accountability visible.
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Whether data may be processed with AI depends on content, purpose, legal basis, contract, provider, storage, access and technical settings. A blanket rule such as “no personal data” is insufficient.
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Traceability means being able to connect a material output to purpose, input version, model, prompt, sources, checks, changes and approvals. Logging all raw data is neither always necessary nor always permissible.
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Strong AI use cases combine measurable value, suitable task structure, available data, manageable risk and an accountable process. Prioritisation starts with the problem, not the tool.
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AI can prepare interviews, structure documents, reformulate requirements, find contradictions and generate test ideas. Domain negotiation, prioritisation and approval remain human responsibilities.
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AI accelerates understanding of code, data models, interfaces and documents. It does not replace executable behaviour, production telemetry or experienced domain knowledge.
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AI can translate business questions into draft SQL and explain results. Reliability requires schema context, permission boundaries, controlled execution and domain plausibility checks.
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AI supports option generation, documentation, interface analysis and test design. Architectural quality still comes from explicit requirements, reviewable decisions and real system behaviour.
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