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Build an AI unit converter as two cooperating parts: let the model interpret a request such as “convert 5 feet to meters,” but have your application validate the units, perform the arithmetic with documented conversion factors, and apply a stated rounding rule. This keeps conversational input convenient without treating generated text as the authority for the result.

What the AI should—and should not—do

Use the AI layer to turn natural language into a structured request: the amount, source unit, target unit, and any necessary clarification. Use ordinary application code to check that the units are supported, select the factor, calculate, and format the answer.

This separation follows the function-calling pattern described in the OpenAI API function-calling documentation and Google’s Gemini API function-calling documentation. In Google’s described workflow, the model requests a function, the application executes it, and the result is returned to the model. Function calling connects a model to your code; it does not make the model itself a dependable conversion engine.

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Define the converter’s scope first

Choose a manageable set of quantities for an initial release. Length, area, volume, mass, time, temperature, and speed are useful candidates; this is a product-scoping choice, not a category list mandated by NIST. NIST’s reference organizes conversion factors by unit and by quantity or scientific field, and its tables illustrate how many named units and variants a broader converter may need to handle. See NIST SP 811, Appendix B: Conversion Factors.

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For each supported unit, keep an explicit catalogue containing its display name, symbol, accepted aliases, quantity type, and any distinctions required to interpret it correctly. Reject or clarify unsupported requests rather than silently guessing.

Make ambiguous names explicit

Names that sound interchangeable may refer to different units. NIST distinguishes, for example, avoirdupois ounces, troy ounces, U.S. fluid ounces, and Imperial fluid ounces. A request for “ounces” may therefore need a follow-up: do you mean a unit of mass or a volume measure, and, for fluid ounces, which system? Store those distinctions in the catalogue and make the selected definition visible in the response.

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Use documented factors and a deliberate rounding policy

NIST’s Metric (SI) Program describes conversion as a process involving multiplication or division by a numerical factor, selection of significant digits, and rounding. Its Unit Conversion guidance also makes clear that conversion is more than changing the label beside a number.

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Use an authoritative factor reference such as NIST SP 811 when building the catalogue. NIST marks some factors as exact and notes that others are rounded to the significant digits shown. Preserve that distinction in your data and avoid implying greater precision than the factor supports. Define how the application handles significant digits and rounding, including what it does when a user does not specify a desired precision.

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For transparency, consider showing the chosen factor and a short calculation explanation alongside the answer. That helps a user see what the application interpreted and how it reached the result without requiring the model to invent an explanation after the fact.

Connect natural-language requests to application code

  1. Define a conversion function. Give it structured inputs such as amount, source unit, and target unit. Keep its supported-unit catalogue and conversion logic in the application.
  2. Ask the model to interpret the request. Configure the model to call the conversion function when it can identify the required inputs. Do not ask it to supply an unverified conversion result as a substitute for execution.
  3. Validate before calculating. Check that the amount is valid, both units are supported, and they represent compatible quantities. If a unit is ambiguous or missing, ask the user to clarify.
  4. Execute the conversion in code. Select the documented factor or conversion rule, calculate the value, and apply the published significant-digit and rounding policy.
  5. Return a clear answer. Include the converted value and target unit; where useful, include the interpretation, factor, or concise calculation. If the request cannot be handled safely, explain what needs clarification.

OpenAI and Google document function calling as a way to connect models with application functions. The application’s code—not the model’s generated prose—should remain responsible for validating inputs and producing the numerical result.

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Give the AI layer a reason to exist

A conventional converter is already available in Google Search: users can enter a conversion in Search, as described in Google Search Help. NIST also publishes conversion guidance and a reference card, including its 2024 Metric Conversion Card.

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Those alternatives make it important to explain what your AI interface adds. It could accept conversational phrasing, handle follow-up questions, or explain which unit definition it selected. These are design options, not established evidence of user demand; choose features that serve your intended audience rather than adding AI for its own sake.

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Design checks before release

  • Coverage: Publish which quantities and unit variants the converter supports; make unsupported conversions explicit.
  • Interpretation: Test that aliases map to the intended unit and that ambiguous terms trigger clarification.
  • Arithmetic: Keep factors and calculation rules under application control, and verify that incompatible quantities are rejected.
  • Precision: State the significant-digit and rounding policy, and ensure the displayed precision does not overstate the factor’s precision.
  • Transparency: Show enough detail for a user to identify the selected units and understand the result.
  • Vendor behavior: Function-calling APIs and model availability can change. Check the current OpenAI or Google documentation for the API and model you choose.

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