Editing Prompt Engineering (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
LLMs are next-token predictors — they complete text in a manner consistent with their training distribution. A prompt is not a command but a '''context that constrains the probability distribution''' over possible continuations. When you write a clear, structured prompt, you are steering the model toward the region of its output space that contains useful, accurate responses.

'''Why prompts matter so much''': The model's behavior is entirely determined by its weights plus its input context. Since you can't change the weights at inference time (without fine-tuning), the prompt is your only lever. Small changes — adding "think step by step," restructuring information, or providing a clear role — can swing response quality dramatically.

'''Chain-of-thought''' works because LLMs trained on vast amounts of human-written text have learned that reasoning traces precede correct conclusions in textbooks, solutions, and technical writing. By prompting "let's think step by step," you nudge the model into the portion of its output distribution where reasoning traces are followed by correct answers.

'''The anatomy of an effective prompt''':
<syntaxhighlight lang="text">
[System/Role]     → Who the model is and what constraints it operates under
[Context/Input]   → Background information, document, or data relevant to the task
[Task/Instruction]→ What exactly to do, stated clearly and unambiguously
[Format]          → What the output should look like (JSON? Numbered list? Table?)
[Examples]        → (Optional) 1–5 input-output demonstrations
[Output Cue]      → A partial beginning of the expected response to prime generation
</syntaxhighlight>

The more precisely each of these components is specified, the less the model must infer — and inference is where errors enter.
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