What is Chain of Thought?
Asking the model to reason step by step before giving the final answer, which raises accuracy on math, planning, and debugging. Keep the reasoning in thinking tokens and return only the conclusion to users.
Related terms
Thinking Budget
The token allowance reserved for internal model reasoning before answering. Larger budgets suit hard multi-step problems; BYOB prices thinking tokens separately so runaway reasoning shows up in metering.
Few-Shot Prompting
Showing the model two or more input-output examples inside the prompt so it imitates the pattern. Examples beat paragraphs of instruction for format, tone, and edge-case handling in builder and support tasks.
Hallucination
When a model states something false with confidence, usually from parametric memory without grounding. Grounded, cited answers hallucinate less, which is why engines prefer quotable sources.
Temperature
Sampling randomness for generation: low values make output focused and repeatable, high values make it varied and surprising. BYOB chat configs default near 0.5 while tool calls use 0.3 for steadier structured actions.
Structured Output
Forcing model replies into a machine-readable shape such as JSON with fixed fields. Required wherever AI output feeds code, databases, or UI components, since free text breaks parsers the moment formatting drifts.
JSON Schema Mode
A structured-output setting where the caller supplies a JSON Schema and the model must conform to it. Types, required fields, and enums are validated automatically, which removes most post-processing repair code.