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Technical messages written for engineers rarely land well with product, design, or leadership. Teams waste time in back-and-forth clarifications — or worse, misread the urgency and miss deadlines. This prompt takes any raw internal message and rewrites it for your target audience and tone: clear, structured, and with next steps that are impossible to miss. Just fill in 5 inputs.

How the prompt works

The system prompt sets the LLM up as a communication consultant and requires six improvements in every rewrite: preserved detail, improved clarity, matched tone, better structure, added context, and clear action items. Without that structure, models either over-simplify (losing key technical detail) or produce generic rewrites that don’t fit the audience. The user prompt takes five variables — original message, target audience, desired tone, context, and primary goal — so every rewrite is shaped by who needs to read it and what it needs to achieve.

System prompt

Sets the LLM’s role and defines the six improvements it must apply in every rewrite.

User prompt

Five variables that shape the rewrite for the right audience, tone, and outcome.
Quick update: API issue impacting Q2 launch timelineHi team,I wanted to loop everyone in on a technical issue affecting our Q2 release timeline and get us aligned on next steps.What’s happening: Our API is hitting rate limits during peak usage times, causing connection errors for our mobile app. Think of it like a highway during rush hour — we’re getting more traffic than our current lanes can handle.The numbers:
  • Our system can handle 1,000 requests per minute
  • We’re seeing 1,200–1,500 requests during busy periods
  • This is preventing our mobile team from completing their final testing
Impact: This is blocking mobile integration testing and could delay our Q2 launch if not resolved this week.Proposed solutions:
  1. Quick fix: Increase our rate limits to handle current traffic
  2. Better fix: Implement smart retry logic so the app handles busy periods gracefully
  3. Bonus: Optimise database connections for better performance
Next steps:
  • Engineering will implement option 1 by Wednesday for immediate relief
  • Option 2 is planned for next sprint as a permanent solution
  • Mobile team can resume testing by Thursday
Questions or concerns? Drop them here or ping me directly.

Import into Adaline

This prompt comes with a ready-to-import Adaline project file. It includes the prompt, dataset, and evaluators, all pre-configured.

Evaluations and dataset

Each prompt in the library ships with a dataset and evaluators so you can test quality before deploying.

Evaluators

Two failure modes, three evaluators: one for rewrite quality, one for audience fit, one for length.

Rewrite quality

Checks that all six required improvements are applied in every output.

Audience fit

Checks that the language and framing match the stated target audience.

Response length

Guards against over-long rewrites. A good internal message respects the reader’s time.
A rewritten message should be shorter and easier to read than the original. If it’s longer, the clarity goal wasn’t met.

Dataset

Four scenarios across different audiences and communication goals — each row maps directly to the five variables in the user prompt.