Large language models can produce fluent answers that are inconsistent, incomplete, or confidently wrong. To test them effectively, you need to understand what happens beneath the interface and how those behaviors change your testing approach.
LLM for Testers is a practical two-module course built around a real AI lab. You will learn how an LLM processes text as tokens, generates one token at a time, works within context limits, and produces outputs that may vary between runs. You will also explore how training data, prompts, fine-tuning, retrieval-augmented generation, and model choice affect behavior, quality, cost, and risk.
Module 1: The LLM Big Picture explains the essential concepts in plain language. You will learn how to identify the parts of an LLM-powered system, locate likely sources of failure, test variable outputs, and distinguish repeatability from correctness.
Module 2: The LLM Play Lab turns those concepts into hands-on testing. After each short demonstration, you will work with a live model to examine token usage, context limits, hallucinations, cut-off answers, typo robustness, similarity scores, training effects, and regression risks. You will also practice using answer keys and evidence to evaluate results and decide whether a problem calls for a prompt change, RAG, fine-tuning, or a different model.
Each module can be taken independently. Module 1 includes approximately 65 minutes of learning across nine sections. Module 2 includes 21 core exercises taking approximately 2 hours and 20 minutes, plus two optional exercises. No coding is required.
Co-developed by Shade of Hue and 3th Floor Studios.