EugeneXiang/prival

🚀 PriVAL: Prompt Input VALidation Toolkit

PriVAL is a lightweight and extensible toolkit for evaluating the quality of prompts for LLMs.
It provides multi-dimensional scoring and improvement suggestions, helping you write better prompts that deliver more reliable model outputs.

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✨ Features

• Multi-dimensional scoring: Covers clarity, ambiguity, injection risk, relevance, and more.
• Pluggable detectors: Each dimension is modular—easy to extend or customize.
• One-line evaluation: Just evaluate_prompt(prompt) to get structured scores and suggestions.
• Flexible config: Easily enable/disable dimensions and adjust weights or thresholds.
• Report generation: Output in JSON / Markdown / HTML formats.

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📦 Installation

# Basic (recommended)
pip install prival

# Install a specific version
pip install prival==0.1.9

# Full version (includes spaCy-based analysis)
pip install prival[full]

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⚠️ macOS or lightweight environments may encounter issues with spaCy or language-tool-python.
If you’re not using syntax/structure-related dimensions, install the base version only.

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🧪 Quick Example

from prival import evaluate_prompt

prompt = "Please write a gentle yet firm resignation letter."
result = evaluate_prompt(prompt)

print(result["total_score"])
print(result["clarity"])
print(result["suggestions"])

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🛠️ Project Structure

prival/
├── config.yaml               # Global config: dimensions, weights, thresholds
├── core.py                   # Main logic: detector routing + aggregation
├── detectors/                # Each validation dimension as standalone module
├── scoring.py                # Weighted score logic
├── report.py                 # Output as Markdown / HTML
├── utils/                    # NLP helpers (syntax, keywords, embeddings)
└── tests/                    # Unit tests + example prompts

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🧩 Config Example

enabled_dimensions:
  - clarity
  - ambiguity
  - step_guidance
  - injection_risk
  # ...
weights:
  clarity: 0.15
  ambiguity: 0.10
  step_guidance: 0.10
  injection_risk: 0.15
  # ...
thresholds:
  clarity: 0.6
  injection_risk: 0.5

📈 Output + Reporting

Each result contains: • score: a float value (0.0 ~ 1.0) • suggestions: concrete suggestions for improvement Example output:

{
  "clarity": { "score": 0.9, "suggestions": [] },
  "step_guidance": { "score": 0.3, "suggestions": ["Add step-by-step hints."] },
  "total_score": 0.72
}

To export a nice visual report:

from prival.report import generate_html

generate_html(result, "report.html")

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🤖 CLI (coming soon)

prival-cli evaluate "Your prompt text here"

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📚 License

MIT License. Feel free to fork, extend, or integrate into your own LLM projects.

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💌 Feedback

Issues, suggestions, or PRs are warmly welcomed: https://github.com/EugeneXiang or ping me on Hugging Face

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Happy prompting! 🎉 Let your prompts shine ✨