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OpenAI's o3 uses extended chain-of-thought reasoning internally, consuming 'thinking tokens' that are billed separately. A single complex reasoning query can consume 10,000-50,000 thinking tokens. Always test o3 usage on small samples before scaling to avoid surprise bills.

Chain-of-thought prompting (asking the model to 'think step by step') dramatically improves reasoning accuracy.

Improve complex reasoning by asking the model to 'think step by step' before giving its final answer. This simple instruction prompts LLMs to break down the problem-solving process, often resulting in more accurate answers.

For complex reasoning, you can often trigger CoT by simply appending 'Let's think step by step.' to your prompt. The model will then show its reasoning process before the final answer.

Large language models exhibit emergent abilities that weren't explicitly trained for, such as few-shot learning, chain-of-thought reasoning, and code generation, which appear at certain model scales.

Improve complex reasoning by asking the model to 'think step by step' before giving its final answer. This simple instruction prompts LLMs to break down the problem-solving process, often resulting in more accurate answers.