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.

Fonctionnalités

Tout ce dont vous avez besoin pour écrire de meilleurs prompts, tester plus intelligemment et optimiser plus vite. À partir d'avril 2026.

Fonctionnalités clés en un coup d'œil

✓9 frameworks de prompt engineering (CO-STAR, CRAFT, RISEN, TRACE, APE, SPECS, Google, RTF)
✓Envoyez à 25+ modèles cloud simultanément (GPT-4o, Claude, Gemini, DeepSeek, et plus)
✓13 types d'analyse de consensus Quorum dans 4 catégories (synthèse, comparaison, qualité, sélection)
✓Détection des hallucinations signale les affirmations qui n'apparaissent que dans un modèle
✓Support des LLM locaux : Ollama, LM Studio, Jan AI, GPT4All, Open WebUI, vLLM, et compatibles OpenAI
✓Confidentialité d'abord : exécution entièrement hors ligne, aucune inscription requise, rien ne quitte votre appareil
✓Comparaison instantanée des réponses côte à côte en temps réel
✓Optimisation automatique des prompts avec 8 techniques de raffinement pour une meilleure sortie IA

Optimisation des prompts

Affinez et optimisez automatiquement vos prompts avec 8 techniques de raffinement éprouvées.

Dispatch multi-modèles

Exécutez des prompts sur ChatGPT, Claude, Gemini et 25+ autres modèles simultanément en parallèle.

Score Quorum

Trouvez des réponses consensuelles avec un score de confiance. Détectez les hallucinations instantanément.

Comparaison instantanée

Voyez les réponses côte à côte en secondes. Fini les tests manuels entre onglets.

Confidentialité d'abord

Option d'exécution locale. Aucune inscription requise. Contrôle total sur vos prompts.

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

Comment consultez-vous les résultats d'optimisation?

Consultez les évaluations de qualité, l'historique des versions et les suggestions d'amélioration pour vos prompts optimisés.

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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

Qu'est-ce que Quorum — Consensus multi-modèles?

Collectez des réponses de 25+ modèles IA, analysez les patterns de consensus et synthétisez les insights selon différentes perspectives.

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)

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Comment fonctionne PromptQuorum en 3 étapes?

Trois étapes simples pour de meilleurs prompts et des décisions IA plus intelligentes.

Choisir un framework

Sélectionnez un framework de prompt engineering comme Chain-of-Thought, Few-Shot ou CRAFT.

Lancer votre prompt

Envoyez votre prompt à 25+ modèles. Regardez les réponses arriver en parallèle en temps réel.

Comparer & Optimiser

Trouvez des réponses consensuelles, détectez les hallucinations et affinez pour une meilleure qualité.

Lectures connexes

Fonctionnement : Vue d'ensemble du système complet Comparez PromptQuorum avec les alternatives Guides et frameworks de prompt engineering Questions fréquemment posées

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