Testing the AI
LLM & GenAI Evaluation Training for corporate teams
What changes when your team
learns testing the AI.
22% to 94% is the hallucination rate across 26 leading AI models. When AI can sound confident and still be wrong, evaluation isn’t optional.
What your team
walks away with.
Everything your team
will actually cover.
01 GenAI & LLM Foundations for Evaluation
02 LLM Evaluation Frameworks & Lifecycle
03 Core LLM Evaluation Metrics: Relevancy & Faithfulness
04 Hallucination, Toxicity & Bias Detection
05 LLM-as-a-Judge: G-Eval in Practice
06 Building Evaluation Datasets & Golden Sets
07 RAG Evaluation: Context Precision, Recall & Correctness
08 AI Risk Testing: Prompt Injection, Bias & Compliance
09 Capstone: End-to-End LLM Evaluation Suite
Hear from teams
we've trained.
“We received a solid foundation covering generative AI and RAG. The trainer never cut content to stick to the scheduled time and went beyond the allotted hours to cover everything we asked for.”
“The team was able to arrive at the same level of understanding. We're looking forward to launching agents and agentic workflows in the next few sprints, and we would look forward to collaborating again for another engagement.”
Sample trainer
profiles.
Seventeen years spanning data, cloud, automation and DevSecOps, now leading applied AI engineering at a global financial-data enterprise. Published author on generative AI.
Runs cross-functional GenAI programs for QA, dev, DevOps, data and leadership teams across four countries. Internationally certified AI trainer, Singapore-accredited.
Twelve years of hands-on automation across fintech and payments-scale platforms. Speaks and organizes meetups across the European QA community, with 863 learner reviews to date.
Profiles are anonymized at this stage. Full profiles are shared once we scope your program.
REQUEST TRAINER PROFILESChoose how
your team learns.
What makes
us different.
THE TEST TRIBEFrequently asked
questions.
What is LLM evaluation?
LLM evaluation is the practice of systematically scoring a large language model’s outputs for relevancy, faithfulness, hallucination, bias, toxicity and more, using structured metrics and datasets, instead of eyeballing a few sample responses.
How is testing an AI application different from traditional software testing?
Traditional testing checks for a deterministic pass/fail against a fixed expected output. LLM and GenAI applications are non-deterministic, because the same prompt can produce different valid answers, so testing shifts to scoring outputs against metrics like relevancy and faithfulness across many runs, rather than a single expected-value assertion.
Is this training only for QA engineers and testers?
No. While QA and SDET teams are core participants, the curriculum is built for anyone responsible for the quality of an AI product: developers shipping LLM features, data scientists building RAG pipelines, and product managers who need to read and trust an evaluation report before a release.
What tools does this LLM evaluation course cover?
The program covers DeepEval for core metrics and hallucination/toxicity/bias detection, RAGAS for RAG-specific evaluation, and an overview of OpenAI Evals and LangSmith so teams can choose the right framework for their stack.
What are the prerequisites for this training?
Basic Python knowledge is preferred but not required. Prior experience with AI evaluation is not necessary.
Will I get hands-on experience?
Yes. You’ll work on practical labs involving evaluation datasets, LLM output scoring, hallucination and toxicity detection, custom G-Eval rubrics, RAG evaluation, prompt injection testing, and complete evaluation suites.
What is LLM-as-a-Judge?
LLM-as-a-Judge uses an AI model to evaluate another AI model’s outputs against defined criteria or rubrics. You’ll learn how to apply this approach using G-Eval, create custom rubrics, and critically interpret the resulting scores.
What will I be able to do after completing the training?
You’ll be able to build evaluation datasets, define meaningful metrics, run AI evaluations, assess LLM and RAG outputs, identify AI quality and safety issues, and report actionable findings that can help improve AI systems.
Why do AI systems need to be evaluated differently from traditional software?
Traditional software is generally expected to produce deterministic results for a given input. AI systems, particularly LLM-based applications, can produce different outputs for the same prompt. This program teaches you how to work with that non-determinism and evaluate AI using datasets, metrics, rubrics, and structured evaluation processes.
What exactly will I learn to evaluate?
You’ll learn to evaluate AI outputs for relevancy, faithfulness, correctness, hallucination, toxicity, bias, PII leakage, and prompt injection risks. You’ll also learn how to evaluate RAG-based outputs using relevant evaluation metrics.
Let's
talk
Tell us where your team is today. We'll map the shortest path according to your team needs, with the right curriculum, trainers and outcomes committed before we start.