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Prompt Engineering Guide

Mastering Write email
on Qwen 2.5 72B

Stop guessing. See how professional prompt engineering transforms Qwen 2.5 72B's output for specific technical tasks.

The "Vibe" Prompt

"Write an email to my colleague, John, about the project update. Keep it concise. Mention we hit a few snags but are back on track. Ask him if he's available for a quick chat tomorrow afternoon to discuss."
Low specificity, inconsistent output

Optimized Version

STABLE
{ "task": "Email Writing", "recipient": { "name": "John", "relationship": "Colleague" }, "objective": "Project Update", "key_messages": [ "Brief update", "Encountered minor snags", "Currently back on track", "Request availability for chat tomorrow afternoon" ], "tone": "Professional, concise", "call_to_action": "Suggest a quick chat tomorrow afternoon to discuss details." }
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt uses a structured JSON format, explicitly outlining key components like task, recipient, objective, key messages, tone, and call to action. This reduces ambiguity and guides the model more precisely. The chain-of-thought implicitly guides the model to address each structured field, ensuring comprehensive coverage and adherence to requirements. It's more efficient than a narrative prompt for complex tasks, despite the apparent increase in token count for this simple example, because it removes the need for the model to parse and interpret natural language nuances, leading to fewer errors and more direct generation.

-50%
Token Efficiency Gain
The generated email is concise and professional.
The email explicitly mentions hitting snags and being back on track.
The email asks John for his availability for a quick chat tomorrow afternoon.

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