Chain-of-Thought (CoT)
A prompting technique that instructs the AI to reason step-by-step before giving a final answer. Dramatically improves accuracy on complex reasoning, math, and logic tasks.
Why it matters
For math, logic, and multi-step problems, asking an AI to think out loud genuinely improves accuracy. It matters because a model rushed to a one-word answer often gets it wrong, while the same model reasoning through steps catches its own mistakes. It also makes the answer auditable: you can see the reasoning and spot exactly where it went off track instead of guessing.
In practice
Instead of "How many hours between 9:45am and 6:15pm?", add "Think step by step, then give the final answer." The AI lays out the arithmetic, minutes then hours, and lands on 8.5 hours more reliably. For anything with several moving parts, a word problem, a scheduling puzzle, a comparison, that simple phrase noticeably cuts down careless errors and shows you its work.
Related terms
A/B Testing (for Prompts)
Running two or more prompt variations against the same AI model to compare output quality. Data-driven prompt optimization that consistently outperforms intuition-based prompting.
Few-Shot Prompting
Providing 2-5 examples of desired input-output pairs in your prompt before asking the AI to perform the task. Significantly improves output quality for specialized tasks.
Prompt Engineering
The skill of crafting effective AI prompts to achieve desired outputs. Techniques include role-setting, few-shot examples, chain-of-thought reasoning, and constraint specification.
System Prompt
A special instruction set given to an AI model before the user's message, defining the model's persona, behavior rules, output format, and constraints. The foundation of custom AI assistants and chatbots.
Zero-Shot Prompting
Asking an AI to perform a task without providing any examples. Relies entirely on the model's pre-trained knowledge. Works well for common tasks.