Learn to reliably evaluate and validate your Generative AI applications.
This course addresses the critical governance and reliability challenges that stop most AI projects from succeeding.
You will also learn how H2O Eval Studio platform provides a systematic, end-to-end workflow to evaluate, validate, and monitor complex LLM and RAG applications, mitigating risk and ensuring a successful return on your AI investment.
What you'll learn
- Model Risk Management (MRM) Fundamentals
Understand the systematic framework for evaluating AI systems in production and learn why domain-specific evaluation matters more than generic benchmarks.
- Robustness and Adversarial Testing
Test your system's resilience against real-world challenges like typos, grammatical errors, and malicious inputs including prompt injections.
- Mitigation Strategies and Guardrails
Learn practical fixes for detected issues, from adjusting system prompts to implementing guardrails that prevent hallucinations and unsafe responses.
- The H2O Eval Studio Workflow
Understand the complete end-to-end evaluation process for testing and validating GenAI and RAG applications in production.
- Automated Test Generation from Your Data
Use topic modeling to automatically generate test cases grounded in your actual documents - no synthetic datasets or manual question writing required.
- Advanced LLM Evaluation Techniques
Learn evaluation methods for detecting hallucinations, measuring answer relevance, and identifying toxicity, bias, and data leakage.
Course Playlist on YouTube
Recording from our successful Prague Meetup on Evaluating & Validating Generative AI Models with EvalStudio! This session brought together AI enthusiasts to explore how to measure and build trust in GenAI systems. Participants completed an interactive quiz with an impressive 7.9/10 average score and received official H2O.ai University certificates. 🔗 Learn more about EvalStudio: evalgpt.ai 📜 H2O.ai University: https://h2o.ai/university/
