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Giskard leads GenAI Evaluation in France 2030's ArGiMi Consortium

The ArGiMi consortium, including Giskard, Artefact and Mistral AI, has won a France 2030 project to develop next-generation French LLMs for businesses. Giskard will lead efforts in AI safety, ensuring model quality, conformity, and security. The project will be open-source ensuring collaboration, and aiming to make AI more reliable, ethical, and accessible across industries.

Blanca Rivera Campos

News

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Understanding Hallucination and Misinformation in LLMs

Blog

A Practical Guide to LLM Hallucinations and Misinformation Detection

Explore how false content is generated by AI and why it's critical to understand LLM vulnerabilities for safer, more ethical AI use.

David Berenstein

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Illustration of AI vulnerabilities and risk mitigation in Large Language Models (LLMs) for secure and responsible deployment.

Blog

A Practical Guide on AI Security and LLM Vulnerabilities

Discover the key vulnerabilities in Large Language Models (LLMs) and learn how to mitigate AI risks with clear overviews and practical examples. Stay ahead in safe and responsible AI deployment.

David Berenstein

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Phare LLM Benchmark - an analysis of hallucination in leading LLMs

News

Good answers are not necessarily factual answers: an analysis of hallucination in leading LLMs

We're sharing the first results from Phare, our multilingual benchmark for evaluating language models. The benchmark research reveals leading LLMs confidently produce factually inaccurate information. Our evaluation of top models from eight AI labs shows they generate authoritative-sounding responses containing completely fabricated details, particularly when handling misinformation.

Matteo Dora

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Testing LLM Agents through continuous Red Teaming

Tutorials

How to implement LLM as a Judge to test AI Agents? (Part 2)

Testing AI agents effectively requires automated systems that can evaluate responses across several scenarios. In this second part of our tutorial, we'll explore how to automate test execution and implement continuous red teaming for LLM agents. Learn to systematically evaluate agentic AI systems, interpret results, and maintain security through ongoing testing as your AI application evolves.

Jean-Marie John-Mathews, Ph.D.

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How to implement LLM as a Judge to test AI Agents? (Part 1)

Jean-Marie John-Mathews, Ph.D.

Tutorials

Secure AI Agents: Exhaustive testing with continuous LLM Red Teaming

Blanca Rivera Campos

Blog

Giskard announces Phare, a new open & multi-lingual LLM Benchmark

Matteo Dora

News

DeepSeek R1: Complete analysis of capabilities and limitations

Matteo Dora

News