Evaluation of LLM Applications: How Do You Know It Actually Works?

Data Science Dojo · Beginner ·📄 Research Papers Explained ·6h ago
Join us for a practical webinar on LLM evaluation frameworks and strategies for measuring the quality, reliability, and performance of AI applications, including chatbots, AI agents, and RAG systems. 💡 What we’ll cover: • Hallucinations, prompt sensitivity, and hidden failure modes • Human evaluation vs automated evaluation • Benchmark testing and regression workflows • Evaluating chatbots, AI agents, summarization, and RAG systems • Introduction to RAGAS and key LLM evaluation metrics • Measuring faithfulness, relevance, groundedness, and latency • Monitoring LLM applications in production 🛠 Hands-on exercise included: Participants will evaluate a small LLM/RAG assistant using structured rubrics and compare human evaluation with automated RAGAS scores. Perfect for AI engineers, developers, data scientists, and technical leaders working with LLM applications and AI systems.
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related AI Lessons

The ABCs of reading medical research and review papers these days
Learn to critically evaluate medical research papers by accepting nothing at face value, believing no one blindly, and checking everything
Medium · LLM
#1 DevLog Meta-research: I Got Tired of Tab Chaos While Reading Research Papers.
Learn to manage research paper tabs efficiently and apply meta-research techniques to improve productivity
Dev.to AI
How to Set Up a Karpathy-Style Wiki for Your Research Field
Learn to set up a Karpathy-style wiki for your research field to organize and share knowledge effectively
Medium · AI
The Non-Optimality of Scientific Knowledge: Path Dependence, Lock-In, and The Local Minimum Trap
Scientific knowledge may be stuck in a local minimum, hindering optimal progress, and understanding this concept is crucial for advancing research
ArXiv cs.AI
Up next
NVIDIA New AI Is An Efficiency Monster
Two Minute Papers
Watch →