// score prompt outputs with simple rubrics
Evaluate AI prompt outputs with custom rubrics. Score clarity, relevance, completeness, and more. Free browser-based prompt quality assessment tool.
Each criterion is scored 1–10. Weight determines its contribution to the final score.
Ready to evaluate
Fill in your prompt, output, and rubric — then click EvaluatePaste your original prompt and the AI-generated output you want to evaluate.
Use a preset or define custom criteria with names, weights, and descriptions.
Rate each dimension from 1–10, then click Evaluate to get a weighted final score.
The Prompt Output Evaluator lets you score AI-generated responses against structured rubrics. Define what "good" looks like — clarity, relevance, tone, accuracy — assign weights, and score. The tool calculates a weighted composite and exports your data.
A prompt output evaluator is a tool that helps you systematically score AI-generated responses using defined criteria — called a rubric. Instead of judging outputs by feel, you break quality into measurable dimensions like clarity, relevance, and completeness, each scored on a 1–10 scale.
Each criterion in your rubric has a weight — a number representing how important that dimension is relative to others. The final score is a weighted average: criteria with higher weights have more influence on the total. For example, if "accuracy" has weight 3 and "tone" has weight 1, accuracy counts three times more toward the final score.
Yes — use the Export JSON button to download your rubric and all scores. You can reload a JSON file in future sessions by pasting its contents, or share it with teammates for consistent evaluation standards.
Three presets are included: Default (clarity, relevance, completeness, accuracy, tone), Minimal (quality and usefulness only), and Code Review (correctness, readability, efficiency, edge-case handling). You can also add, remove, or edit any criterion freely.
No. All processing is done entirely in your browser using JavaScript. Your prompts, outputs, and scores never leave your device. There are no accounts, no tracking, and no server calls.
Evaluate multiple prompt/output pairs one at a time, exporting a JSON after each. Collect these into a folder or merge them programmatically. The JSON schema is consistent across exports, making it easy to load into Python or Excel for analysis.
A prompt output evaluator is a structured tool for assessing the quality of AI-generated responses. As large language models (LLMs) become embedded in production workflows, the need to measure and compare output quality has grown enormously. Rather than relying on gut feelings or inconsistent peer review, a rubric-based evaluator gives teams a systematic, repeatable method for scoring AI outputs.
This tool lets you define your own evaluation rubric — a set of named criteria like clarity, relevance, completeness, or accuracy — and score each dimension from 1 to 10. Each criterion carries a weight, so dimensions that matter more to your use case contribute proportionally more to the final score.
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LLM outputs can vary dramatically based on model version, temperature settings, system prompts, and the phrasing of user messages. Without a consistent evaluation framework, it's easy to draw incorrect conclusions — assuming one model is better because one particular output felt better, even when the other model performs better on average across many prompts.
Structured rubric scoring addresses this by forcing evaluators to assess specific, predefined qualities rather than making holistic judgments. This approach is used in academic NLP benchmarking, enterprise AI governance, and prompt engineering workflows at scale.
Start by pasting your original prompt into the left panel and the AI-generated output into the right panel. Next, set up your rubric. You can load one of the three built-in presets — Default, Minimal, or Code Review — or build a custom rubric by clicking "Add Criterion."
For each criterion, assign a name (e.g., "Factual Accuracy"), a weight (how important this dimension is, from 1–5), and optionally a short description of what each score level means. Then score each criterion from 1 to 10 based on how well the output meets that standard.
Click Evaluate to generate a weighted composite score, a visual breakdown by criterion, and a grade (Excellent / Good / Acceptable / Poor). You can add free-text evaluator notes, then export everything as JSON or CSV for storage and analysis.
One of the most valuable uses of this tool is systematically building an evaluation dataset. If you're developing a product powered by an LLM, you'll eventually need to answer questions like: Did our prompt improvements actually improve output quality? Does Model A outperform Model B for our specific use case?
The answer requires data. Evaluate representative prompt/output pairs — ideally across different models, prompt versions, or time periods — and export each evaluation as JSON. Over time, these files become a structured dataset you can analyze with Python, load into a spreadsheet, or visualize in a dashboard.
The JSON export from this tool uses a consistent schema: prompt, output, rubric definitions, per-criterion scores, weights, final weighted score, grade, and notes. This consistency makes it easy to merge dozens of evaluations and run aggregate analysis.
The quality of your evaluation depends heavily on rubric design. Here are principles that experienced prompt engineers follow:
While every use case is different, these criteria appear frequently in prompt evaluation frameworks:
There are two broad approaches to evaluating LLM outputs: automated metrics and human (manual) evaluation. Automated metrics like BLEU, ROUGE, or BERTScore compare outputs to reference answers mathematically. They're fast and scalable but struggle with open-ended tasks where there's no single correct answer.
Manual evaluation — which this tool supports — involves humans reading and scoring outputs. It's slower but captures nuances that automated metrics miss: whether an explanation is genuinely helpful, whether a joke lands, whether a summary preserves the author's intent. For many real-world applications, some manual evaluation is irreplaceable.
A common best practice is to use both: run automated metrics for quick regression testing, and use structured manual evaluation (with tools like this one) to calibrate quality standards and validate that automated metrics correlate with human judgment.
Prompt engineers use them to measure the impact of prompt changes — comparing outputs before and after a system prompt revision to verify that quality actually improved. ML engineers and researchers use them to benchmark competing models or fine-tuned variants against a common rubric. Content teams at companies using AI-assisted writing use them to QA outputs before publication. Educators and trainers use them to teach prompt engineering, giving students a structured way to reflect on output quality.
As AI becomes more embedded in workflows, teams that establish evaluation discipline early are better positioned to maintain quality as models, prompts, and use cases evolve. This tool is a lightweight starting point for building that discipline.