We Launched Open Source to Evaluate RAG-Powered Chatbots

We’re excited to introduce the open-source version of ContextCheck, a tool designed to evaluate Retrieval-Augmented Generation (RAG) chatbots effectively. This release aligns with our mission to advance AI usability, focusing on transparency, performance, and groundedness in AI-driven interactions.

What is ContextCheck?

ContextCheck empowers developers and organizations to assess RAG-powered chatbots’ ability to deliver accurate, contextually relevant responses. It is an open-source solution available on GitHub, designed to analyze how well chatbots integrate knowledge retrieval with conversational AI capabilities.

Key features include:

• Hallucination Detection: Identifies instances where chatbots generate unsupported or fabricated responses.
• Groundedness Evaluation: Measures the model’s reliance on verifiable sources during response generation.
• Custom Scoring Metrics: Allows users to adapt evaluation parameters to specific business needs or data environments.

Why Open Source?

Open-sourcing ContextCheck stems from our belief that collaboration accelerates innovation. By making this tool freely available, we aim to:

1. Encourage Transparency: Facilitate the evaluation of RAG systems for fairness, accuracy, and safety.
2. Promote Customization: Enable developers to tailor the tool for niche use cases or integrate it into proprietary workflows.
3. Foster Community Growth: Engage AI enthusiasts and professionals in refining and expanding its capabilities.

How ContextCheck Works

Built to simplify evaluation processes, ContextCheck uses a combination of metrics to test a chatbot’s retrieval and generation mechanisms. Here’s how it operates:

1. Knowledge Source Integration: Link your knowledge base to ContextCheck for analysis.
2. Interaction Simulation: Generate test prompts and observe chatbot responses.
3. Result Analysis: Review metrics on grounding, hallucination rates, and retrieval accuracy, presented in a detailed dashboard.

For organizations relying on LLMs, ContextCheck offers clarity on chatbot performance, ensuring users receive accurate, data-backed responses.

Applications and Benefits

ContextCheck is especially useful for industries that prioritize reliability in chatbot interactions, such as:

• Customer Support: Evaluating chatbots that assist users with technical queries.
• Healthcare: Ensuring responses are grounded in verified medical literature.
• Legal: Testing chatbots for accurate retrieval of case law or regulatory documents.

By using ContextCheck, teams can enhance trust in their AI systems, improve user satisfaction, and maintain compliance with data integrity standards.

Join the Community

This initiative invites developers, researchers, and businesses to contribute to the project on GitHub. Explore the tool, propose enhancements, or share insights from real-world applications.

With ContextCheck, we’re taking a significant step toward demystifying RAG-powered AI systems. Whether you’re building an internal AI assistant or deploying large-scale chatbot solutions, this tool is your partner in delivering reliable, impactful AI interactions.

Start exploring ContextCheck today! Visit our GitHub repository for more details and documentation.