Measure the Repeated Cost of System Instructions and Tool Definitions

How to Use This Tool

Separate reusable instructions from user content to expose repeated token overhead. Calculate repeated AI prompt overhead from requests, system tokens, tool-schema tokens and cacheable share. Uses editable inputs and runs locally.

The failure Prompt Overhead is designed to catch

A long system prompt and tool schema are paid repeatedly unless the provider and request shape actually qualify for caching. The boundary is the job stated in Measure the Repeated Cost of System Instructions and Tool Definitions; Prompt Overhead is not intended to score or transform a different workflow.

The Prompt Overhead input contract

The fields used for this specific operation are Requests per period, System prompt tokens, Tool schema tokens, Cacheable share %. Keep the source values beside the Prompt Overhead result, because replacing the original would remove the evidence needed to reproduce or reverse the operation.

• For Prompt Overhead, Requests per period starts at 100000 in the worked case; replace that example with the matching source value.
• For Prompt Overhead, System prompt tokens starts at 1500 in the worked case; replace that example with the matching source value.
• For Prompt Overhead, Tool schema tokens starts at 2500 in the worked case; replace that example with the matching source value.
• For Prompt Overhead, Cacheable share % starts at 80 in the worked case; replace that example with the matching source value.

Worked result for Prompt Overhead

The executable case called Default decision scenario expects out: 80.0. Verify that observation before entering real material, and then change one Prompt Overhead field at a time so an unexpected direction or formatting change can be traced to a specific input.

Reading the Prompt Overhead output

It combines requests per period, system prompt tokens, tool schema tokens and cacheable share % into one decision result using the formula explained on the page. Apply that answer only when Requests per period, System prompt tokens, Tool schema tokens, Cacheable share % describe the same scope and format as the worked operation. If the source uses different units, quoting, nesting, timing or account rules, a plausible-looking Prompt Overhead output is not sufficient validation.

Assumptions attached to Prompt Overhead

• Prompt Overhead assumes that all inputs describe the same unit or reporting period unless the field explicitly says otherwise.
• Prompt Overhead assumes that the model includes only the four visible inputs and does not infer hidden platform charges.
• Prompt Overhead assumes that the page never calls an AI model; token ratios, prices, limits and observed rates are user-supplied planning assumptions.

If one of these Prompt Overhead assumptions is false, keep the result as a diagnostic rather than production or decision data, and choose an implementation that explicitly supports the missing rule.

Evidence maintained for Prompt Overhead

The recorded reference is NIST — AI Risk Management Framework. Reopen that source when the definition, format, fee or policy behind Prompt Overhead changes; private configuration and downstream acceptance still have to be checked in the user's own system.

Where Prompt Overhead runs

The named operation executes in browser JavaScript without an ecech calculation API. For Prompt Overhead, local execution reduces transmission but does not control browser extensions, device security or the destination where the result is pasted, so sensitive inputs still require the user's normal handling rules.