Advanced Prompt Engineering
Chain-of-Thought (CoT)
Forcing models to reason step-by-step.
Interview: High - Key reasoning technique
Making LLMs Think Step by Step
CoT Reasoning Flow
Chain-of-Thought prompting improves LLM performance on reasoning tasks by asking the model to show its work. Instead of jumping to an answer, the model breaks down the problem into intermediate steps.
Why It Works
LLMs generate one token at a time. When forced to generate reasoning steps, each step provides additional context for the next token prediction, leading to more accurate final answers.
Variants
- Manual CoT: You provide step-by-step examples
- Zero-shot CoT: Simply add "Let's think step by step" to your prompt
- Self-consistency: Generate multiple CoT paths and vote on the most common answer
- Tree of Thought: Explore multiple reasoning branches, backtrack when needed
Use Cases
Mathematical reasoning and word problems
Multi-step logical deductions
Code debugging (explain the bug step by step)
Complex decision-making with multiple factors
Common Mistakes
Using CoT for simple tasks where it wastes tokens without benefit
Not validating the reasoning steps — the model can produce plausible but wrong reasoning
Forgetting that CoT increases output token count and thus cost