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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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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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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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Blog

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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Increasing trust in foundation language models through multi-lingual security, safety and robustness testing

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

During the Paris AI Summit, Giskard launches Phare, a new open & independent LLM benchmark to evaluate key AI security dimensions including hallucination, factual accuracy, bias, and potential for harm across several languages, with Google DeepMind as research partner. This initiative is meant to provide open measurements to assess trustworthiness of Generative AI models in real applications.

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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Giskard Vision: Enhance Computer Vision models for image classification, object an landmark detection

Benoît Malézieux

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