What prompt frameworks does PromptQuorum include?

PromptQuorum includes 9 built-in prompt engineering frameworks: CO-STAR, CRAFT, RISEN, TRACE, APE, SPECS, Google, and RTF. Each framework structures your input into specific fields that improve output quality.

What is Quorum consensus scoring?

Quorum analysis collects responses from multiple AI models and scores them for consensus. When 5+ independent models agree on an answer, confidence is far higher than when a single model responds alone.

How many AI models can I dispatch to simultaneously?

PromptQuorum dispatches to 25+ cloud AI models including GPT-4o, Claude 4.6 Sonnet, Gemini 2.5 Pro, Mistral Large, DeepSeek, and others. You can also add custom API endpoints.

What types of Quorum analysis are available?

PromptQuorum offers 13 Quorum analysis types across 4 categories: Synthesis (Consensus Summary, Weighted Merge, Key Points), Comparison (Contradiction Detection, Confidence Scoring, Best Answer Selection), Quality (Hallucination Detection, Fact Verification, Tone Analysis), and Selection (Consensus-based ranking).

How does hallucination detection work?

Hallucination Detection flags claims that appear in only one model's response or that contradict factual consensus across multiple models. When 5+ independent models disagree on a specific claim, it is flagged as a potential hallucination.

Can I use PromptQuorum offline with local LLMs?

Yes. PromptQuorum supports all major local LLM runtimes: Ollama, LM Studio, Jan AI, GPT4All, Open WebUI, vLLM, KoboldCpp, and any OpenAI-compatible endpoint. Run entirely in your browser—nothing leaves your device.

功能

编写更好提示词、更智能测试、更快优化所需的一切。截至 2026 年 4 月。

核心功能一览

✓9 种提示词工程框架（CO-STAR、CRAFT、RISEN、TRACE、APE、SPECS、Google、RTF）
✓同时分发到 25+ 个云模型（GPT-4o、Claude、Gemini、DeepSeek 等）
✓4 个类别的 13 种 Quorum 共识分析类型（合成、比较、质量、选择）
✓幻觉检测标记仅在一个模型中出现或与共识相矛盾的声明
✓本地 LLM 支持：Ollama、LM Studio、Jan AI、GPT4All、Open WebUI、vLLM 及 OpenAI 兼容端点
✓隐私优先：完全离线执行、无需注册、数据不离开设备
✓所有分发模型的实时并行回答对比
✓自动提示词优化，包含 8 种改进技术以获得更好的 AI 输出

提示词优化

通过 8 种经过验证的优化技术自动改进和优化您的提示词。

多模型分发

同时并行地在 ChatGPT、Claude、Gemini 及 25+ 个模型上运行提示词。

Quorum 评分

通过置信度评分在各模型中找出共识答案。即时检测幻觉内容。

即时对比

几秒内并排查看所有回答。无需在浏览器标签间手动测试。

隐私优先

本地执行选项。无需注册。完全掌控您的提示词。

Prompt Optimizer

Choose a framework, optimize your prompt, and compare across AI models

Selected provider

OpenAI GPT-4

Select Framework

Your Prompt

💡 Tip: Be specific about your requirements, context, and desired output format.

Primary Provider

📚 Need help optimizing your prompt? View prompt engineering best practices

⚙️ Optimization engine powered by OpenAI GPT-4

如何查看优化结果?

查看优化后提示词的质量评估、版本历史和改进建议。

← Back to Prompt

Optimization Results

Review, refine, and optimize your prompt with AI assistance

Original Prompt

Explain the concept of prompt engineering to someone who has never heard of it before. Focus on practical examples and real-world applications.

Quality Score:

★★★★★

Optimized Prompt

Version History

Explain prompt engineering to beginners through: 1. DEFINITION: A technique for structuring AI requests to get better, more accurate responses 2. KEY CONCEPT: "How you ask matters as much as what you ask" - Vague prompts → generic responses - Specific, well-structured prompts → tailored, useful answers 3. REAL-WORLD EXAMPLES: ✗ Bad: "Tell me about AI" ✓ Good: "Explain how large language models work in 3 paragraphs, with one example of practical application" 4. PRACTICAL TECHNIQUES: • Be specific about context and constraints • Break complex requests into steps • Provide examples of desired output • Ask the AI to think through its reasoning 5. WHY IT MATTERS: Saves time, improves accuracy, enables advanced AI use cases End with: "You're already doing prompt engineering—now do it better."

📚 Teaching Explanation

Prompt engineering is the practice of carefully structuring your requests to an AI to get the most useful, accurate, and relevant responses. Think of it as learning the "language" that AI models understand best. The better you explain what you want, the better results you'll get—just like talking to a person, but with explicit clarity about structure, examples, and constraints.

Quick Refinements

Quality Assessment

Current Quality:

★★★★★

Strengths:

• Clear structure with numbered sections
• Concrete examples provided for beginners
• Actionable techniques listed
• Good use of formatting (bullets, emphasis)

Areas for Improvement:

• Could include more diverse examples
• Interactive elements would enhance engagement
• Transition between sections could be smoother

Quorum — 多模型共识是什么?

收集 25+ 个 AI 模型的回答，分析共识模式，从不同视角综合洞察。

Quorum — Multi-Model Consensus

Collect responses from multiple LLMs, analyze patterns, and synthesize insights across models.

✓

Collect

✓

Analyze

Results

Step 3: Analysis Results

✓ Analysis complete. Consensus and Differences patterns identified across 3 models.

Consensus

SHARED THEMES ACROSS ALL MODELS: 1. CORE DEFINITION - All models agree: structuring input to get better LLM output - Common emphasis: clarity, specificity, instruction following 2. KEY BENEFIT - Saves time and improves response quality - Enables more advanced use cases - Critical skill for effective AI interaction 3. PRACTICAL APPROACH - Context and constraints matter - Breaking complex tasks into steps - Providing examples of desired output CONFIDENCE: Very High (100% alignment on core concepts)

Differences

VARIATIONS IN EMPHASIS: OpenAI's GPT-4: - Emphasized: "optimization" and refinement process - Focus: Iterative improvement and testing Anthropic's Claude: - Emphasized: "understanding model interpretation" - Focus: Theory of how models process language Google's Gemini: - Emphasized: "methodology for maximizing utility" - Focus: Practical outcomes and ROI OBSERVATION: Different models highlight their own strengths - GPT-4 focuses on iteration (refiner's mindset) - Claude focuses on understanding (teacher's mindset) - Gemini focuses on outcomes (engineer's mindset)

Quality Assessment

COMPARATIVE ANALYSIS: DEPTH RANKING: 1. Anthropic (Claude) - Most thorough explanation of WHY, best for learning 2. OpenAI (GPT-4) - Most practical advice, best for doing 3. Google (Gemini) - Most concise, best for quick reference COMPREHENSIVENESS: - All three covered fundamentals adequately - None mentioned advanced techniques (chain-of-thought, few-shot examples) - All lacked concrete failure examples TARGET AUDIENCE FIT: - Beginner: Claude (most educational) - Practitioner: GPT-4 (most actionable) - Executive: Gemini (most concise)

Save to History•Start New Analysis•Clear Session

PromptQuorum 如何通过 3 步工作?

三个简单步骤，获得更好的提示词和更智能的 AI 决策。

选择框架

选择提示词工程框架，如 Chain-of-Thought、Few-Shot 或 CRAFT。

运行提示词

将提示词发送给 25+ 个模型，实时并行接收所有回答。

比较与优化

找出共识答案，检测幻觉内容，优化以获得更好的输出质量。

功能

核心功能一览

提示词优化

多模型分发

Quorum 评分

即时对比

隐私优先

Prompt Optimizer

如何查看优化结果?

Optimization Results

Original Prompt

Optimized Prompt

Quality Assessment

Quorum — 多模型共识是什么?

Quorum — Multi-Model Consensus

Step 3: Analysis Results

PromptQuorum 如何通过 3 步工作?

选择框架

运行提示词

比较与优化

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