Prompt: Simplify Technical Writing for a General Audience
Turn dense technical or specialist writing into something a non-expert can actually follow, without losing the parts that matter.
The prompt
You are a science communicator who translates expert writing for non-experts without dumbing it down.
Objective: Rewrite the supplied text so that {{AUDIENCE — e.g. a new employee with no background in this area}} can understand it.
Context: The original is written for {{ORIGINAL AUDIENCE — e.g. specialists in this field}}.
Constraints:
- Replace jargon with plain language, or explain it in one clause the first time it's used
- Do not remove any conclusion, number or limitation stated in the original
- Use short sentences and concrete examples over abstractions
- If simplifying would lose accuracy, keep the technical term and explain it instead of substituting a wrong analogy
Example: replace "asynchronous non-blocking I/O" with "the program keeps working while it waits for a slow task, instead of freezing."
Output format: Return the simplified version only, matching the original's paragraph structure.
Before finishing, check that no factual claim, number or limitation from the original was dropped or changed.
Original text:
{{PASTE ORIGINAL TEXT HERE}}
Example
Input
AUDIENCE — e.g. a new employee with no background in this area: a new employee with no background in this area ORIGINAL AUDIENCE — e.g. specialists in this field: specialists in this field PASTE ORIGINAL TEXT HERE: The system employs asynchronous non-blocking I/O to mitigate thread contention under high concurrency, though this may introduce increased tail latency under certain workload distributions.
Output
The system uses a method that lets it keep working on other tasks while it waits for slow operations — like reading a file — instead of freezing until each one finishes. This helps avoid a bottleneck when many requests arrive at once, because no single task blocks the others. However, this approach can make a small number of requests take noticeably longer than usual (engineers call this "tail latency"), and it happens only with certain patterns of incoming work, not all of them. So while the method helps the system cope when many requests arrive at once, a few requests may still be slower than expected.
Why it works
- Protecting every conclusion, number and limitation from the original stops the model quietly rounding away a caveat during simplification.
- Allowing a kept technical term with a plain-language explanation avoids the common failure of a wrong analogy replacing an accurate term.
- The finishing check verifies no factual claim or limitation was dropped, catching silent accuracy loss before it reaches the reader.
Why the basic version fails
“Explain this more simply” often produces something simpler and also quietly wrong — hedges disappear, a “may cause X in some cases” becomes “causes X,” and the nuance that made the original accurate gets flattened for readability.
What this prompt fixes
Explicitly protecting conclusions, numbers and limitations means simplification happens at the language level, not the content level. The instruction to keep a technical term rather than force a wrong analogy avoids the common failure of a simplification that’s easy to read and factually misleading.
Tips per model
- ChatGPT is good at finding plain-language equivalents; check it hasn’t softened a limitation into a false confidence.
- Claude tends to preserve caveats and hedged language more faithfully when explicitly told to.
- Paste the whole original paragraph, not a summary of it — a summary has already lost some of the nuance this prompt is designed to preserve.