Neura saysPrompt engineering is the craft of phrasing inputs to get reliable outputs from an LLM.
An LLM's output is steered entirely by its input, so how you ask matters enormously. Good prompts are specific, give context and constraints, show the desired format, and assign a role. It's empirical — small wording changes shift results, so you iterate and test. Prompting is the cheapest, fastest lever before fine-tuning or RAG; master it first.
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Output is steered by the input
Be specific: context, constraints, format, role
Empirical — iterate and test
Cheapest lever before tuning/RAG
The Overfit Ogre attacks — common mistakes
Vague prompts expecting mind-reading
Not specifying output format
Assuming one prompt works across all models
Boss battleImprove a vague prompt by adding role, context, constraints, and format.
Example code
<!doctype html><html><head><meta charset="utf-8"></head>
<body style="background:#06040d;color:#e6e0ff;font-family:monospace;padding:20px"><pre>vague: "summarize this"
better: "You are an editor. Summarize the text below
in 3 bullet points, plain language."</pre></body></html>