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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

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Phare LLM Benchmark - an analysis of hallucination in leading LLMs

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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

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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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Implementing LLM as a Judge to test AI agents

Tutorials

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

Testing AI agents effectively requires automated systems that can evaluate responses across several scenarios. In this first part of our tutorial, we introduce a systematic approach using LLM as a judge to detect hallucinations and security vulnerabilities before deployment. Learn how to generate synthetic test data and implement business annotation processes for exhaustive AI agent testing.

Jean-Marie John-Mathews, Ph.D.

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Secure AI Agents: Exhaustive testing with continuous LLM Red Teaming

Testing AI agents presents significant challenges as vulnerabilities continuously emerge, exposing organizations to reputational and financial risks when systems fail in production. Giskard's LLM Evaluation Hub addresses these challenges through adversarial LLM agents that automate exhaustive testing, annotation tools that integrate domain expertise, and continuous red teaming that adapts to evolving threats.

Blanca Rivera Campos

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

Matteo Dora

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DeepSeek R1: Complete analysis of capabilities and limitations

Matteo Dora

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[Release notes] Giskard integrates with LiteLLM: Simplifying LLM agent testing across foundation models

Blanca Rivera Campos

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AI Liability in the EU: Business guide to Product (PLD) and AI Liability Directives (AILD)

Stanislas Renondin

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