Not all LLM based applications are a user facing free form chat.

If you take an LLM that makes 10 tool calls in a row for an evaluation, any reduction in unpredictable drift is welcome. Same applies to running your prompt through DSPy Optimizer. [0] Countless other examples. Basically any situation where you are in control of the prompt, the token level input to the LLM, so there's no fuzziness.

In this case, if you would've eliminated token level fuzziness and can yourself guarantee that you're not introducing it from your own end, you can basically map out a much more reliable tree or graph structure of your system's behavior.

[0]: https://dspy.ai/#2-optimizers-tune-the-prompts-and-weights-o...