Prompt Template Testing
Prompt template testing validates the reusable prompt structures an application builds at runtime — the fixed instructions plus variable slots for user input, retrieved context, or chat history — checking that they render correctly for edge-case inputs like empty values, special characters, or very long strings, and still produce the intended model behavior once filled in.
Most production AI features don't send a static prompt — they assemble one from a template plus dynamic values, and that assembly step is a normal piece of software with normal bugs: a variable that's empty breaks the template's structure, an unescaped user input with a curly brace collides with the templating syntax, or a very long retrieved document pushes the assembled prompt over the model's context window.
Testing the template itself, independent of the model's response, means checking the rendered prompt for a range of inputs — empty strings, unusually long strings, strings containing the template's own delimiter syntax, non-English characters — the same way you'd test any other string-templating code, before ever sending it to the model.
The second layer is testing that the filled-in template still produces the intended behavior — a template that was tuned and tested with short example inputs can behave differently once real user input, much longer or differently phrased, fills the same slot, so template tests need to use realistic production-shaped data, not just the examples used to originally write the prompt.
