What is Chain-of-Thought? - Definition & Meaning
Learn what Chain-of-Thought (CoT) is, how step-by-step reasoning in LLMs improves, and when to use it for complex AI tasks.
Definition
Chain-of-Thought (CoT) is a prompting technique where an LLM is asked to write out its reasoning process step by step before reaching a conclusion. This improves performance on mathematical, logical, and multi-step problems.
Technical explanation
Instead of answering directly, the model generates intermediate steps: "Let's think step by step... First... Then... Therefore...". CoT can be zero-shot ("Think step by step") or few-shot with reasoning examples. It works by forcing the model to explicitly decompose; larger models (GPT-4, Claude) benefit most. Variants: self-consistency (multiple chains, majority vote), tree-of-thought (explore multiple paths). CoT increases token usage and latency.
How AVARC Solutions applies this
AVARC Solutions applies Chain-of-Thought to complex analysis, code review, and decision support. We use few-shot CoT for consistent output and combine it with structured output for parseable results.
Practical examples
- A math problem where the model first identifies given values, applies the formula, then computes the answer.
- A code review where the model analyzes each line, identifies potential bugs, and reaches a verdict.
- A logic puzzle where the model rules out options through reasoning before giving the final answer.
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