OpenAI o1 & o3-mini Prompting: Reasoning Effort, Structured Outputs & Algorithmic Synthesis

1. What to Avoid: 2022 CoT Buzzwords

Older models like GPT-3.5 and early GPT-4 required behavioral scaffolding such as "Think step-by-step and write out your reasoning before answering".

On o1 and o3-mini, these phrases are counterproductive. The models are trained with Reinforcement Learning to allocate their own hidden reasoning tokens. Adding conversational fluff merely consumes your context window budget.

2. Structured Outputs with Pydantic & JSON Schema

OpenAI o1 and o3-mini natively support response_format: { type: "json_schema", json_schema: { ... } }. Because the model verifies schema invariants during its internal thinking phase, JSON emission is 100% deterministic with zero missing commas or truncated keys.

# Python OpenAI SDK Example
response = client.beta.chat.completions.parse(
    model="o3-mini",
    reasoning_effort="high",
    messages=[
        {"role": "user", "content": "Solve this LeetCode Hard problem..."}
    ],
    response_format=AlgorithmicSolutionSchema,
)