Giskard
Official@giskard-ai · France
Protect your company against quality & security issues in AI systems.
Agent Skills by Giskard
Showing 3 vetted skills indexed across 2 GitHub repositories.
Giskard-AIrag-evaluator
Evaluate RAG systems for groundedness, relevance, and citation accuracy.
Giskard-AIscenario-generator
Generate adversarial test scenarios and suites for AI agents using giskard.checks.
Giskard-AIdiataxis-documentation
Classifies technical documentation into Diataxis-based types and improves clarity.
Frequently Asked Questions About Giskard
FAQPage SchemaWhat specific validation tasks does Giskard perform for RAG systems?▼
Giskard evaluates RAG systems by measuring groundedness, relevance, and citation accuracy. It identifies potential hallucinations and retrieval failures, ensuring that generated responses remain faithful to the provided source documents and maintain high factual integrity during enterprise deployment.
Who is the target persona for these validation capabilities?▼
The primary users are machine learning engineers, security researchers, and quality assurance specialists. These professionals utilize the platform to harden model deployments against adversarial inputs and ensure that system outputs meet rigorous compliance and performance standards before moving to production.
How does the documentation classification feature function?▼
The documentation module analyzes technical content and classifies it into specific Diataxis-based types, such as tutorials, how-to guides, explanations, or references. This process improves information architecture and ensures that technical documentation remains clear, consistent, and accessible for end-users and developers.