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

Building with large language models is easy – effective LLM evaluation is the real challenge. Unlike traditional software, LLM applications can generate fluent but incorrect responses, behave inconsistently across prompts, and fail in subtle ways that are difficult to detect with standard testing methods.
In this webinar, we’ll explore practical LLM evaluation frameworks and strategies for measuring the quality, reliability, and performance of AI applications.

As organizations increasingly deploy AI-powered chatbots, AI agents, and retrieval-augmented generation (RAG) systems, robust evaluation methods are essential for ensuring trustworthy outputs and better user experiences.
We’ll begin by examining common LLM evaluation challenges, including hallucinations, prompt brittleness, hidden failure modes, and the difference between responses that sound correct versus responses that are actually correct.

From there, we’ll cover practical evaluation techniques including human evaluation, automated evaluation, benchmark testing, rubric-based scoring, and production monitoring.
We’ll also introduce RAGAS, a widely used framework for RAG evaluation, and explore how it measures important metrics such as faithfulness, answer relevance, context precision, and context recall.

### What We Will Cover:

• Core challenges in LLM evaluation
• Hallucinations, prompt sensitivity, and unreliable AI outputs
• Defining evaluation criteria and success metrics for AI applications
• Human evaluation, automated evaluation, and benchmark testing
• Building test datasets and regression testing workflows
• Evaluating chatbots, AI agents, summarization, and RAG systems
• Introduction to RAGAS and LLM evaluation metrics
• Measuring accuracy, relevance, faithfulness, groundedness, and latency
• Monitoring LLM applications in production and detecting quality drift

### Hands-On Exercise:

Participants will evaluate a small LLM or RAG-based assistant using structured rubrics and example prompts. They will assess response quality, grounding, completeness, and relevance, then compare human evaluation with automated RAGAS scores.
The exercise will demonstrate why LLM evaluation requires both human judgment and automated scoring, and how prompt design, retrieval setup, and chunking strategies impact AI application performance.

### Who Should Attend:

• AI engineers and developers building LLM applications
• Data scientists and machine learning practitioners
• Product managers and technical leaders working with AI systems
• Anyone interested in LLM evaluation, RAG systems, and AI reliability

Join us for a practical session on LLM evaluation and leave with actionable frameworks for building reliable, measurable, and production-ready AI applications.