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Giskard's Open Source Framework: A Comprehensive Guide to AI Model Evaluation

Giskard, a French startup, innovates in AI with an open-source testing framework for large language models, enhancing model safety by identifying biases and security risks, and ensuring compliance with regulations like the EU's AI Act.

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Hailing from France, Giskard emerges as an innovative startup focused on revolutionizing the AI industry with its open-source testing framework. This framework is specifically tailored for large language models, providing developers with critical alerts about potential biases, security vulnerabilities, and the propensity of models to generate harmful or toxic content.

In an era where AI models garner immense attention, ML testing systems' significance is escalating, especially with impending regulations like the EU's AI Act. Giskard aligns with these regulatory frameworks and is a pioneer in offering a developer tool dedicated to enhancing testing efficiency, thus helping companies avoid severe penalties by ensuring compliance with necessary standards.

Context and Need for Giskard's Framework

In AI and ML, there's increasing recognition of the need for rigorous testing systems. The anticipation of stricter regulations, such as the EU's AI Act, has propelled the demand for tools that ensure compliance and mitigate risks associated with AI models. This scenario is where Giskard’s testing framework shines, catering to the need for more efficient and focused developer tools in the AI sector​​​.

Key Components of Giskard's Framework

giskard's open source framework evaluates ai models before they're pushed into production
  1. Open Source Python Library: The core of Giskard’s framework is its open-source Python library, compatible with prominent tools in the ML ecosystem like Hugging Face, MLFlow, and TensorFlow. This library facilitates the integration of Giskard’s testing capabilities into various LLM projects, particularly those involving retrieval-augmented generation (RAG)​​​​​​.

  2. Test Suite Generation: Post-setup, Giskard aids in creating a comprehensive test suite that addresses a broad spectrum of issues, including performance, biases, hallucinations, misinformation, data leakage, and more. This suite becomes a regular part of the model evaluation, ensuring thorough scrutiny of each iteration​.

  3. Integration in CI/CD Pipelines: A critical aspect of Giskard’s framework is the integration of tests into continuous integration and continuous delivery (CI/CD) pipelines. This feature allows automatic testing with each new code iteration, promptly identifying and addressing any issues​​.

  4. Customization for Specific Use Cases: The framework provides the flexibility to tailor tests according to the end use case of the model. This customization ensures that the testing is as relevant and effective as possible, particularly in specialized applications like climate change information retrieval using RAG models​.

  5. AI Quality Hub and Real-Time Monitoring Tools: Giskard offers an AI Quality Hub for debugging and comparing LLMs and a real-time monitoring tool called LLMon. These tools enable developers to track and rectify issues in production, ensuring the delivery of accurate and reliable AI responses​.

Addressing the Challenges in AI Quality Management

Giskard's framework tackles several challenges inherent in AI model development:

  • The Complexity of AI Models: With the rising complexity of AI systems, particularly LLMs, Giskard’s framework provides an essential service in automating the detection of vulnerabilities and managing the quality across the AI model lifecycle.

  • Balancing Automation and Human Supervision: The framework combines automated processes with human oversight, ensuring that broad and domain-specific quality criteria are met. This approach is vital in managing the infinite edge cases that AI models can encounter.

  • Navigating Development Trade-offs: AI development involves numerous experiments and trade-offs. Giskard’s framework aids in this trial-and-error process by providing a structured approach to test and validate various technical choices.

  • Regulatory Compliance and Collaboration: As global AI regulations become more stringent, Giskard's framework assists in creating the necessary compliance documentation, catering to stakeholders like auditors, data scientists, and IT professionals.


Giskard's open-source framework is a pioneering solution in AI quality management. By providing tools for comprehensive testing, debugging, automation, and monitoring, Giskard addresses the critical needs of AI model development in an increasingly regulated and ethically conscious world.

Its focus on balancing automation with human supervision, customization for specific use cases, and integration into existing development pipelines makes it a valuable asset for any organization developing AI models. As AI advances, tools like Giskard's framework will play a pivotal role in ensuring AI technologies' safety, reliability, and compliance.