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Evals for Groq

8 evaluation packs covering adversarial robustness, safety gates, workflow quality, and operator-level checks for Groq AI products.

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About Groq

Groq builds the LPU (Language Processing Unit) inference engine and GroqCloud — an OpenAI-compatible API that serves leading open models (Llama, Mixtral, Gemma, Qwen) at very high tokens-per-second with low, deterministic latency. Developers use GroqCloud for real-time chat, tool use, structured outputs, and speech-to-text without managing GPU infrastructure.

Employees

~300

Industry

AI Inference Platform

Headquarters

Mountain View, CA

Website

groq.com

Available eval packs for Groq

8 packs ready to run.

Why eval Groq AI

Groq's AI features ship behind brand promises about accuracy, safety, and reliability. Buyers and integrators need to know those promises hold up under adversarial prompts, edge-case workflows, and the long tail of real customer inputs — not just the demo path.

The Corsac eval library for Groq measures four dimensions teams care about most when deploying ai platform agents:

  • Adversarial robustness — does the agent resist prompt injection, jailbreaks, and social-engineering attempts?
  • Workflow quality— does it complete the task buyers were shown in the demo, on inputs that don't look like the demo?
  • Safety gates — does it escalate or refuse when it should, and only then?
  • Operator quality — does it preserve analyst trust by surfacing the right context at the right time?

Every eval pack above is hand-authored against Groq's public product surface and runnable in Corsac with your own data.