Most prompt-rewriter tools stop at ChatGPT, but the vague-prompt problem shows up on Gemini and Perplexity too — and the fix differs in one important way. On Gemini, specificity mostly shapes structure and length. On Perplexity, because it's search-grounded, specificity also changes which sources get retrieved. This post gives a worked example for each, the research behind why specificity matters, and the one-click way to do it without retyping.
Why specificity matters — and why more isn't always better
The link between prompt specificity and output quality is well-established in the research. Recent multi-task evaluations of LLMs treat instruction specificity as a core robustness dimension, and broader work on prompt engineering ties output quality directly to the structure and clarity of the input. A 2025 empirical study on prompt patterns in ChatGPT-generated code (Aria et al., arXiv:2504.13656) puts the swing at up to 45.48 percent between optimal and suboptimal prompts on the same task.
But the relationship isn't "more is better." A 2025 analysis of prompt bloat found that unrelated or excessive context introduces noise that can pull a model off-task and lower answer quality. The useful move is adding the right specifics — audience, format, constraints, context — not padding length. That distinction matters more on Gemini and Perplexity than people expect.
Gemini: specificity shapes structure
Gemini handles long, structured prompts well, which means it rewards clear formatting instructions and punishes vagueness with sprawl.
| Before | After |
|---|---|
| Summarize this report for me. | Summarize this report for a product manager who hasn't read it. Give five bullets: the main finding, the two numbers that matter most, one risk, and one recommended next step. Keep it under 120 words. |
The vague version restates the report's own summary. The rewritten one fixes the audience, the structure, and a length cap — so you get something you can paste into a status update instead of something you have to re-summarize.
Perplexity: specificity shapes which sources it pulls
Perplexity is different because it retrieves and cites live sources. Your prompt doesn't just shape wording — it shapes the source set. Vague in, broad sources out.
| Before | After |
|---|---|
| Is intermittent fasting good for you? | Summarize the strongest peer-reviewed evidence from the last three years on intermittent fasting and metabolic health in adults. Note where studies disagree, and flag anything based only on animal models. Cite sources. |
The first query pulls a centroid of general health sites. The second steers Perplexity toward recent, peer-reviewed sources and asks it to surface disagreement — which is where the useful answer lives. On a search-grounded engine, specificity is partly a retrieval instruction, not only a writing instruction.
A note on early in-house testing
In our own internal before/after testing across the four supported sites — early data from a small beta cohort, not a controlled study — the largest, most consistent gains from rewriting showed up on open-ended, underspecified prompts: summarization, planning, and research queries. Already-specific prompts saw little change, which is why PrePrompt grades a prompt before rewriting and leaves a strong one alone. We're treating these as directional observations and plan to publish a fuller benchmark; we're flagging the cohort size here so the numbers aren't read as more than they are.
The same button on all four sites
You can write every rewrite above by hand. The catch is doing it on every prompt, on every site, while remembering each model's quirks.
A chrome extension solves the consistency problem. PrePrompt adds the same Rewrite button next to Send on ChatGPT, Claude, Gemini, and Perplexity — type as usual, click Rewrite, send the better version. It lands in the same box in about half a second, and Undo restores your original. Free for 30 rewrites a month, no card required.
TL;DR
- The vague-prompt problem isn't ChatGPT-specific — it appears on Gemini and Perplexity too.
- On Gemini, specificity shapes structure and length. On Perplexity, it also changes which sources get retrieved.
- More context isn't automatically better — research on prompt bloat shows excess context can hurt. Add the right specifics, not maximum length.
- To keep the workflow identical across all four sites, a chrome extension like PrePrompt adds the same Rewrite button to each.
About the author. Yashdeep works on PrePrompt's prompt-evaluation and benchmarking, including the classifier that scores prompts before rewriting. PrePrompt is an open-source (MIT) prompt-rewriting chrome extension covering ChatGPT, Claude, Gemini, and Perplexity. First-party figures in this post are early beta-cohort observations, labeled as such.
Sources: A Multi-Task Evaluation of LLMs' Processing of Academic Text Input (arXiv:2508.11779); Do Prompt Patterns Affect Code Quality? (EASE 2025, arXiv:2504.13656); The Impact of Prompt Bloat on LLM Output Quality (MLOps Community, 2025).
Related reading: how to rewrite a ChatGPT prompt automatically and the best chrome extension to rewrite your prompts.