Evaluating Prompts Like an Engineer
A prompt is good when it performs consistently, not when one output impressed you.
The prompt
Here are 2 versions of a prompt and 5 test inputs. Run each version mentally against each input. Build a table: input × version → predicted weakness (if any). Then recommend which version ships and what single change would most improve it. Version A: [PASTE] Version B: [PASTE] Test inputs: [LIST]
What to replace
Swap these placeholders for your own details before running the prompt:
[PASTE]your own value[LIST]your own value
Pro tip: Save your test inputs. Every time a prompt fails in production, add that case to the eval set — it compounds.
How to use this prompt
- Copy the prompt using the button above.
- Replace [PASTE], [LIST] with your own details — the more specific you are, the better the output.
- Paste it into Any model and run it.
- If the answer feels generic, add constraints: audience, length, tone, and what to avoid. That single change fixes most weak output.
Learn the technique
Evaluating Prompts Like an Engineer
Module 5 — Expert: Prompt Systems · Prompt Engineering Fundamentals
Related prompts
The 5-Part Prompt Framework — ChatGPT
Every strong prompt combines five parts: Role (who the AI should act as), Context (background info it needs), Task (the specific ask), Format (how you want the output structured), and Constraints (length, tone, things to avoid).
ClaudeThe 5-Part Prompt Framework — Claude
Every strong prompt combines five parts: Role (who the AI should act as), Context (background info it needs), Task (the specific ask), Format (how you want the output structured), and Constraints (length, tone, things to avoid).
ChatGPTZero-Shot vs Few-Shot Prompting — ChatGPT
Zero-shot means asking directly with no examples.
GrokZero-Shot vs Few-Shot Prompting — Grok
Zero-shot means asking directly with no examples.
ChatGPTChain-of-Thought Prompting — ChatGPT
Asking a model to "think step by step" before answering measurably improves accuracy on reasoning, math, and multi-step logic tasks because it forces the model to externalize intermediate steps instead of jumping to a guess..
ClaudeChain-of-Thought Prompting — Claude
Asking a model to "think step by step" before answering measurably improves accuracy on reasoning, math, and multi-step logic tasks because it forces the model to externalize intermediate steps instead of jumping to a guess..
Topics
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Chain-of-thought prompting
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Chain-of-thought prompting improves accuracy on reasoning tasks by asking the model to work step by step. Here is why it works, where it helps most, and when it is pure overhead.
Context engineering
Context engineering is the practice of deciding what information enters a model's context window and in what order. As windows grew, choosing what to include became more consequential than wording the instruction.