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Plain-language AI glossary

Term 22TechniquesMeaning / context / connections

Techniques / Definition

Chain of Thought (CoT)

A prompting technique that improves AI reasoning by instructing the model to break down complex problems into intermediate steps before giving a final answer.

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MeaningThe one-sentence definition.

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ContextHow the idea works in practice.

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UsesWhere the concept becomes useful.

01 / Plain-language context

How Chain of Thought (CoT) works.

Chain-of-thought describes intermediate reasoning steps used to reach an answer. Asking for a concise rationale can help on some tasks, but requesting a long visible transcript is not a universal quality technique and may increase cost, latency, or exposure of irrelevant reasoning. Many current reasoning models manage intermediate work internally.

In production, evaluate the final answer and the evidence needed to trust it. If an explanation matters, ask for a short, auditable justification tied to inputs, calculations, or sources rather than an unrestricted stream of internal reasoning. The right amount of visible rationale depends on the risk and review needs of the job.

02 / Practical uses

Where it helps.

  1. 01Math and logic problems
  2. 02Complex classification with reasoning
  3. 03Multi-step data analysis
  4. 04Debugging and code review
  5. 05Decision-making with explanations
03 / Keep exploring
Prompt EngineeringThe practice of crafting effective instructions for AI models to produce desired outputs consistently.Large Language Model (LLM)A neural network trained on massive text datasets that can generate, understand, and reason about human language.Few-Shot LearningA prompting technique where you provide a small number of input-output examples in the prompt to teach the model the desired behavior.Zero-Shot LearningThe ability of an AI model to perform a task based solely on instructions, without any training examples provided in the prompt.