This tool addresses the challenge of factual inaccuracies in AI-generated content, providing a real-time verification system for LLM outputs. It's designed for developers and users of LLMs who need to ensure the reliability and accuracy of generated text, offering a "Grammarly for facts" experience.

How It Works

The system operates in four stages: claim extraction using an LLM (Claude 3.5 Sonnet), source verification via Exa.ai's search tool to find supporting or refuting web sources, accuracy analysis by the LLM comparing claims against sources, and a clear results display with suggested corrections. This approach leverages a dedicated AI search engine for robust source retrieval and a powerful LLM for nuanced analysis.

Quick Start & Requirements

• Install dependencies: npm install or yarn install
• Run development server: npm run dev or yarn dev
• Prerequisites: Node.js, Exa.ai API key, Anthropic API key.
• Setup involves cloning the repo, installing dependencies, and configuring API keys in a .env.local file.
• Demo: http://localhost:3000/hallucination-detector

Highlighted Details

• Leverages Exa.ai, a search engine optimized for AI applications.
• Utilizes Claude 3.5 Sonnet for claim extraction and accuracy analysis.
• Frontend built with Next.js, App Router, TailwindCSS, and TypeScript.
• Integrates AI capabilities via Vercel AI SDK.

Maintenance & Community

• Developed by Exa.ai.
• No explicit community links (Discord/Slack) or roadmap mentioned in the README.

Licensing & Compatibility

• The README states the tool is "free and open-source" but does not specify a license type.
• Requires API keys for Exa.ai and Anthropic, which may have their own usage terms and costs.

Limitations & Caveats

The project relies on external API keys for Exa.ai and Anthropic, potentially incurring costs and introducing external dependencies. The specific license for the open-source code is not stated, which could impact commercial use.