reasoning-model-prompt-checker

To optimize prompts for O1 and O3-mini reasoning models, evaluate goal clarity, specify return formats like JSON or tables, validate constraints, and adjust parameters like reasoning_effort and max_completion_tokens to ensure stable multi-step outputs.

You need a prompt checker for deep-reasoning models when executing multi-step reasoning, technical problem solving, or format-constrained outputs, ensuring your prompt provides sufficient context and clear structural guidance to prevent failed iterations.

Model selection between O1 and O3-mini depends on task complexity and data volume. Structured prompt diagnosis evaluates your specific requirements to recommend the appropriate reasoning model, ensuring the chosen model aligns with your technical analysis needs.

O1 model outputs fail to return consistent JSON format when prompts lack explicit return format specifications and bounded token usage. Prompt optimization adds format examples and parameter recommendations to constrain outputs and resolve structural inconsistencies.

For O3-mini prompts requiring multi-step reasoning, you should set parameters like reasoning_effort and max_completion_tokens. Prompt optimization analyzes your task to recommend specific parameter configurations that bound token usage and stabilize deep-reasoning outputs.

Yes, you can use the prompt checker to fix clarity and structure issues in existing prompts. It performs a structured diagnosis to identify insufficient context, unclear goals, and missing constraints, then provides optimization suggestions and rewrites for deep-reasoning models.