Prompt engineering defines the task, context and constraints given to a generative model. Clear instructions can improve relevance, but they do not guarantee factual accuracy or provide a security boundary for connected tools.
State the objective, intended reader and required output. Supply the approved information needed for the task and explain how missing evidence should be handled. For a supplier summary, request supported capabilities and unresolved gaps rather than ask for an impressive description.
Test variations on the same representative examples. Examine factual errors, omitted conditions and unsupported claims as well as formatting. Avoid refining a prompt against only one successful example and then assuming it will handle the full workload.
Version prompts and record the model and settings used during evaluation. Validate important fields outside the model and retain review for consequential outputs. A prompt is one part of a controlled application workflow, not a replacement for its checks.
Open Sources Used
This page uses open and institutional references as a frame; the final decision still belongs to the company record, threshold and owner.
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Reading adjacent decision areas keeps the topic from becoming an isolated note.