The State of Prompt Quality
Every prompt evaluated on PromptEval becomes an anonymous data point: one score, 0–100, from a fixed rubric across clarity, specificity, structure, and robustness. This page is the running total. Measured, not surveyed.
Four dimensions. One keeps failing.
Figures below update as new evaluations come in. For citation-stable numbers, use a quarterly edition. Those never change after publication.
34.9% of prompts score below 50. 10.5% reach the production bar.
What the data says
Frozen on July 11, 2026 at n = 1,018. These numbers will not change under you. Cite them freely. Full analysis in the Q3 2026 edition →
The average prompt score. Median: 60. Only 10.5% clear 75, the common bar for production use.
#average-scoreshare the same weak spot: robustness. It averages 31.5/100, half the score of clarity (63) or structure (65). Prompts are written for the happy path.
#robustness-gapof prompts have no defense against messy input. Bad-input resilience averages 30/100; edge-case coverage, 32/100.
#messy-inputfor declaring an output format. Prompts that say what the answer should look like average 60; prompts that don't, 31. One in five still skips it.
#output-formatof prompts include an example. The most underused lever in the dataset, worth +10 points on average.
#examplesof prompts set no constraints. Saying what the model must not do is worth +24 points on average.
#constraintsThe cheapest points people leave on the table
Four things anyone can add to a prompt, ranked by average lift in this dataset. The bar shows how many prompts actually use each one.
Say what the answer should look like: a list, a table, three paragraphs, JSON.
31.3 without
Boundaries and rules: what to avoid, what never to do, hard limits.
40.8 without
Who the model is: role, expertise, point of view.
42.2 without
One worked example of the output you want. The rarest lever, and it is free points.
53.3 without
Who writes what
Share of classified prompts by use case. Segments under n ≈ 150 appear here but don't get standalone claims. Sample discipline is part of the methodology.
Frozen snapshots, quarter by quarter
The hub you're reading updates continuously. Editions are frozen at quarter close and never edited. Cite an edition when you need a number that holds still.
The inaugural edition. n = 1,018 · why 96% of prompts share one weak spot, and the four levers that separate 31 from 60.
First quarter-over-quarter comparison: did robustness move?
How these numbers are made
Every evaluation run on PromptEval contributes one anonymous row: the overall score, four dimension scores, and structural metadata (length, language, whether it has examples, constraints, a persona, an output format). The prompt text itself is never stored in the benchmark dataset.
An LLM judge scores each prompt against a fixed, versioned rubric: eight behaviors, two per dimension. Each dimension is the mean of its two behaviors; the overall score is the mean of all eight. Same judge configuration for every prompt, every time. Rubric changes ship as a new scoring version, never silently.
All eight scored and ranked in the Q3 2026 edition. Only the scores leave the box: the rubric text, weights, and judge configuration stay internal.
This is not "the average prompt on Earth." It is prompts people chose to submit to an evaluator, often because they suspected something was wrong. Scores likely skew lower than the true population. Read every figure here as "prompts submitted for evaluation," never "all prompts."
Use case, prompt type, and language labels come from a separate model pass over ~95% of rows. Unclassified rows count toward totals but not toward breakdowns.
Segments below roughly n = 150 appear in tables but never get standalone headline claims or dedicated pages. When a segment crosses the threshold, it graduates.
Use these numbers
Everything on this page may be republished with attribution and a link (CC BY 4.0). For figures that must never move, cite the Q3 2026 edition.
PromptEval, "The State of Prompt Quality." Live benchmark of 1,025 evaluated prompts. https://prompt-eval.com/state-of-prompt-quality (accessed July 12, 2026).Where does your prompt land?
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