# PromptQuorum

> One prompt. Every model. One verdict. PromptQuorum is a multi-model AI dispatch and consensus tool. Write one structured prompt, send it simultaneously to ChatGPT, Claude, Gemini and 25+ AI models, then run consensus analysis across all responses — hallucination detection, contradiction scoring, best-answer extraction. 100% private: API keys stay in your browser, no data reaches PromptQuorum servers.

## Product

- **Primary use case**: Dispatch a single prompt to 25+ AI models simultaneously and analyze consensus across all responses
- **Category**: AI Tools / Multi-model workspace / Prompt engineering
- **Pricing**: Free. Bring your own API key (BYOM) or use a local LLM. No account required.
- **Platforms**: macOS, Windows (desktop), Web browser
- **Offline support**: Full offline capability via local LLMs (Ollama, LM Studio, Jan AI, GPT4All)
- **Beta launch**: April 2026
- **Developer**: Hans Kuepper — [LinkedIn](https://www.linkedin.com/in/hanskuepper/)

## Supported AI Models

**Cloud providers (25+)**: OpenAI (GPT-4, GPT-4o, o1), Anthropic Claude (3, 3.5, 4), Google Gemini (1.5 Pro, 2.0 Flash), xAI Grok, DeepSeek, Mistral, Cohere, Meta Llama (via Together AI, Groq, OpenRouter), Perplexity, and more.

**Local / self-hosted**: Ollama, LM Studio, Jan AI, GPT4All, Open WebUI, KoboldCpp, vLLM, oobabooga, and any OpenAI-compatible endpoint.

## Core Features

- **9 Prompt Frameworks**: CO-STAR (Singapore GPT-4 competition winner), CRAFT, RISEN, APE, SPECS, TRACE, RTF, Google Prompt, Single Prompt Line — plus 2 fully custom frameworks
- **Dispatch**: One-click simultaneous dispatch to all connected AI services; tabs open in parallel
- **Quorum Analysis (13 types)**: Consensus Summary, Weighted Merge, Atomic Facts Extraction, Overlap Mapping, Contradiction Detection, Confidence Scoring, Completeness Check, Hallucination Detection, Redundancy Elimination, Best Answer Selection, Multi-Model Ensemble, Controversy Flag, Response Ranking
- **Optimization**: AI-powered iterative prompt refinement with 8 one-click refinements and full version history
- **Privacy (BYOM)**: API keys stored only in browser localStorage, never transmitted; zero telemetry, zero tracking
- **Export**: Results export in TXT, MD, JSON, CSV, HTML, PDF

## Pages

- [Homepage](https://www.promptquorum.com): Product overview, 4-stage pipeline, waitlist
- [How It Works](https://www.promptquorum.com/how-it-works): Full workflow — Write, Optimize, Dispatch, Quorum
- [Features](https://www.promptquorum.com/features): Complete feature list with details
- [Compare Models](https://www.promptquorum.com/compare): Why multi-model dispatch matters vs single-model use
- [FAQ](https://www.promptquorum.com/faq): 26 answers covering pricing, privacy, frameworks, hallucination detection, local LLMs
- [Download](https://www.promptquorum.com/download): Desktop app for macOS and Windows

## Prompt Engineering Guides (Core Content)

- [What Is Prompt Engineering?](https://www.promptquorum.com/prompt-engineering/what-is-prompt-engineering): Foundational guide covering the definition, history, core techniques, and why prompt engineering matters in 2026.
- [5 Building Blocks Every Prompt Needs](https://www.promptquorum.com/prompt-engineering/5-building-blocks-every-prompt-needs): The 5 essential structural components of every effective prompt — role, task, input, constraints, output format.
- [Prompt Injection & Security: How to Defend AI Systems](https://www.promptquorum.com/prompt-engineering/prompt-injection-and-security): OWASP LLM #1 attack vector — direct vs indirect injection, jailbreak vs injection, and 5-layer defense framework.
- [How LLMs Actually Work: Tokens, Attention, and Inference](https://www.promptquorum.com/prompt-engineering/how-llms-actually-work): Demystify token prediction, attention mechanisms, RLHF training, and inference for better prompt design.
- [How Prompt Engineering Evolved (2018–2026)](https://www.promptquorum.com/prompt-engineering/how-prompt-engineering-evolved): Historical timeline from few-shot prompting with GPT-3 to modern multi-modal, multi-step, multi-model techniques.
- [Chain of Thought Prompting: Structured Reasoning](https://www.promptquorum.com/prompt-engineering/chain-of-thought-prompting): How step-by-step reasoning prompts unlock complex problem-solving in GPT-4o, Claude, Gemini.
- [Constrained Prompting: Limiting Model Behavior](https://www.promptquorum.com/prompt-engineering/constrained-prompting): Enforce output boundaries, prevent hallucination escape, and make models comply with structured rules.
- [RAG Explained: Retrieval-Augmented Generation](https://www.promptquorum.com/prompt-engineering/rag-explained): How to augment LLM knowledge with external documents; retrieval strategies, prompt engineering for RAG, and hallucination mitigation.

## Blog & Research

- [8 Prompt Frameworks Compared: CRAFT vs CO-STAR vs APE (2026 Guide)](https://www.promptquorum.com/blog/prompt-frameworks): When to use each of the 9 built-in frameworks; strengths, ideal use cases, and effectiveness data for CO-STAR, CRAFT, RISEN, APE, SPECS, TRACE, RTF, Google Prompt, and Single Prompt Line.
- [ChatGPT vs Claude vs Gemini: Side-by-Side Model Comparison](https://www.promptquorum.com/blog/ai-model-comparison): Capabilities, strengths, weaknesses, and best use cases for the major cloud AI models; why sending one prompt to all three reveals more than querying any single model.
- [What Is Quorum? Multi-Model Consensus Analysis Explained](https://www.promptquorum.com/blog/quorum): How consensus scoring works across parallel model responses; the 13 Quorum analysis types and how they detect hallucinations, contradictions, and low-confidence claims.
- [Local AI vs Cloud: Privacy-First Prompt Optimization](https://www.promptquorum.com/blog/local-ai-vs-cloud): When to choose local LLMs over cloud providers; privacy implications, latency tradeoffs, and setup guides for Ollama and LM Studio.
- [How Automatic Prompt Optimization Works](https://www.promptquorum.com/blog/prompt-optimization): The mechanics of AI-powered iterative refinement; how PromptQuorum's 8 one-click refinements improve precision, tone, specificity, and output format.
- [Enterprise Data Privacy with Local LLMs](https://www.promptquorum.com/blog/enterprise-data-privacy): How enterprises use local LLMs for zero-transmission inference; compliance considerations and offline deployment patterns.
- [Research: Impact of Prompt Engineering on AI Output Quality (2024–2026)](https://www.promptquorum.com/blog/research-prompt-optimization-impact): Structured analysis of how prompt engineering affects AI output quality; effectiveness data across frameworks, refinement techniques, and model families.

## Citation

**APA**: Kuepper, H. (2026). PromptQuorum — multi-model AI dispatch and consensus tool. Retrieved from https://www.promptquorum.com

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Last updated: 2026-03-31