Advanced Prompt Engineering Language
5 exercises — Describe chain-of-thought reasoning, few-shot design, system prompt elements, sampling parameters, and prompt injection mitigations.
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A developer shares a prompt with the team: "Let's think step by step. First, identify what type of error this is. Then, determine which component is responsible. Finally, suggest a fix." A colleague says: "That's a classic CoT prompt." Why does chain-of-thought prompting improve accuracy on complex tasks?
Chain-of-thought works by making reasoning visible and sequential.
When a model is prompted to articulate intermediate steps ("let's think step by step"), it must commit each reasoning step to the context before the next. This prevents the model from taking a shortcut to a plausible-sounding answer and forces it to surface errors in logic that would otherwise be hidden inside a single inference step.
Research by Wei et al. (2022) showed CoT dramatically improves performance on arithmetic, commonsense reasoning, and symbolic tasks — gains that do not appear in small models and require sufficient scale to be effective.
CoT has nothing to do with context window size, temperature settings, or safety bypass — it is a prompting technique that shapes the structure of the model's output.
Key vocabulary:
• chain-of-thought (CoT) — prompting technique that elicits step-by-step reasoning before a final answer
• intermediate reasoning steps — explicit sub-conclusions the model writes out before reaching its answer
• shortcut reasoning — a model pattern of jumping to a plausible answer without correct logical grounding
• zero-shot CoT — adding "let's think step by step" without any worked examples
When a model is prompted to articulate intermediate steps ("let's think step by step"), it must commit each reasoning step to the context before the next. This prevents the model from taking a shortcut to a plausible-sounding answer and forces it to surface errors in logic that would otherwise be hidden inside a single inference step.
Research by Wei et al. (2022) showed CoT dramatically improves performance on arithmetic, commonsense reasoning, and symbolic tasks — gains that do not appear in small models and require sufficient scale to be effective.
CoT has nothing to do with context window size, temperature settings, or safety bypass — it is a prompting technique that shapes the structure of the model's output.
Key vocabulary:
• chain-of-thought (CoT) — prompting technique that elicits step-by-step reasoning before a final answer
• intermediate reasoning steps — explicit sub-conclusions the model writes out before reaching its answer
• shortcut reasoning — a model pattern of jumping to a plausible answer without correct logical grounding
• zero-shot CoT — adding "let's think step by step" without any worked examples