AI in marketing
Keep the rules clear. Let the model explain.
When an application combines calculations and generated language, separating their responsibilities makes the result easier to test and understand.
A score and its explanation have different jobs.
A deterministic calculation should produce the same result from the same defined inputs. Generated language can help explain that result, but it should not quietly change the underlying rules or invent a stronger conclusion.
This distinction is useful for assessments, calculators, summaries and workflow tools. It gives reviewers separate things to verify.
Build the boundary into the system.
- Define the input fields and validation rules.
- Calculate the result using explicit logic.
- Pass only the necessary result and context into the explanation step.
- Check the explanation for unsupported claims or changed values.
- Provide a readable fallback if generation fails.
Do not let polished language overstate the method.
A career reflection tool should not describe itself as a validated diagnostic merely because its report sounds authoritative. A cost calculator should not predict future revenue from a simple division. The product’s wording must remain within what the method establishes.
Test the contract between the layers.
Known inputs should produce known calculations. Explanations should preserve the calculation, identify limitations and avoid claims outside the supplied context. Test zero values, missing fields and contradictory inputs before considering the experience complete.
Keep the result useful without the model.
Where practical, the user should still be able to understand the basic result if the generation service is unavailable. That keeps an explanatory enhancement from becoming an unnecessary dependency for a calculation the application already knows how to perform.
See a career-assessment implementation · Try the growth diagnostic