Compare
Simo vs prompting a chat model and parsing the answer
The default way to get a judgment from a model is to prompt it and parse what it writes. It works until it doesn’t.
Side by side
| Prompt and parse | Simo | |
|---|---|---|
| You send | A prompt describing the format you hope for | A situation and typed questions |
| You get back | Free text | A probability per option, or a typed value |
| Parsing | Required; formats drift | None; the answer is already typed |
| Confidence | Unquantified | Calibrated |
| Speed | Seconds for a reasoning model | Milliseconds |
| Cost | Billed for every word written | No words written for decisions |
Where prompt-and-parse breaks
- The model adds a sentence, changes a label, or wraps the answer in markdown, and the parser fails.
- Two runs give different formats, so edge cases only show up in production.
- There is no threshold: “probably” and “definitely” both parse to “yes”.
The Simo way
Ask, and read the probability. Every question is typed, so the answer arrives in a shape your code already understands. See how Simo works and the question types.
Frequently asked questions
Do I still need a chat model?
For writing, chat and open-ended tasks, yes. For judgment calls inside software, Simo gives a typed, calibrated answer instead.