Thinking effort: quick answers or careful ones
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AI providers and models
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Step 1: Chat model
Why this matters here
Start with the familiar quick-response mode so the learner can recognize when an answer prioritizes speed over extended deliberation.
In short
An AI model optimized for rapid, natural back-and-forth dialogue and immediate task execution, without an extended internal reasoning phase.
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Step 2: Reasoning model
Why this matters here
Reasoning models introduce the contrasting behavior of spending more internal work on a hard answer before replying.
In short
A specific type of AI model that generates internal chain-of-thought tokens to plan, verify, and self-correct before outputting a final response.
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Step 3: Reasoning effort
Why this matters here
Effort makes that tradeoff a setting the learner can choose instead of treating it as a fixed property of the model.
In short
A setting that controls how much a model thinks before answering; more effort generally takes longer and uses more output tokens.
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Step 4: Token
Why this matters here
Tokens explain the unit consumed by both the answer and the model's hidden thinking work.
In short
The fundamental unit of data processed by an LLM, roughly equivalent to a word or part of a word in English.
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Step 5: Token budget
Why this matters here
A budget closes the path by showing how a team constrains the cost and duration of more careful reasoning.
In short
The strict allocation and monitoring of how many API tokens an AI agent or application is allowed to consume during a specific task or workflow.