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This accompanies our article on OpenAI integration.

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How do you make a model response useful in software that

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needs predictable fields and controlled follow-up actions?

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A fictional application receives a fault report.

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The model proposes a category and summary.

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However, the report does not contain a valid equipment reference for

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subsequent processing.

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The application sends only necessary text, receives a proposal and

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validates the output.

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Only after the agreed review may an authorised next step change another system.

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Document permitted fields, types and values.

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Specify what missing information means.

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Additional fields or an unknown category are not silently accepted as

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valid input for the application.

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A correctly structured response can invent an equipment reference or

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misrepresent the fault.

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Check its meaning against the input and relevant records, separately

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from validating its structure.

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A model cannot replace missing authorisation.

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The application checks the user and business rules.

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Without a valid equipment match, this report goes to a reviewer

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rather than automatic execution.

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Record refusals, limits and timeouts against the task.

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Identify who follows up unfinished work.

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Monitor usage and total response time without unnecessarily retaining

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the original input.

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Start with representative examples and known expected checks.

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Test changes to prompts, model selection or tool definitions before

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the revised version influences everyday operational work.

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An integration involves more than a successful API call.

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Our source article on OpenAI integration explains how output

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validation, data processing and error handling form an application feature.