This Gen AI course equips QA engineers and test leads to validate and stress-test LLM-powered systems, not just traditional software. You'll cover LLM foundations, GPT-system interfaces, and inference parameters to understand what you're actually testing, then dive deep into RAG pipelines — chunking, embeddings, hybrid retrieval, and reranking — to know where failures hide. A strong focus on RAG evaluation methodology gives you frameworks and metrics to systematically test retrieval quality, groundedness, and hallucination. Later modules cover multi-vector retrieval, GraphRAG, and LangChain workflows so you can test advanced architectures with confidence. Built for QA professionals who need to design test strategies, benchmarks, and guardrail checks for production GenAI applications.

A practical AI strategy programme for business leaders. Covers LLM literacy, RAG as a competitive capability, cost governance, vendor evaluation, and risk frameworks — anchored to real decisions, not theory.

Most statistics courses teach theory first and AI later, if at all. This course flips that. Every lesson starts from an everyday example and lands on a real GenAI or agent use case. It covers how sure you can be of a score, whether a new prompt is really better, how models turn probabilities into words, how retrieval finds the right documents, and how to evaluate and monitor AI in production. It's built for tech professionals, with no heavy maths or code on the slides.

This Gen AI course takes developers, architects, and tech leads from LLM fundamentals to production-grade retrieval systems. You'll master interfacing with GPT-class models, prompt engineering and inference tuning, and the full RAG stack — embeddings, advanced chunking, hybrid retrieval with reranking, and evaluation. Later modules go deep on multi-vector retrieval, GraphRAG, and LangChain-based RAG orchestration. Built for hands-on builders who need to design and ship reliable, production-ready GenAI systems, not just prototype demos. By the end, you'll be equipped to architect and evaluate retrieval-augmented applications at scale.

Build autonomous, stateful AI agents that reason, plan, and act — and ship them to production. This is an advanced, hands-on course for AI/ML engineers who already work with LLMs and want to move beyond single-shot prompting into designing true agentic systems: multi-step reasoning, tool use, memory, human-in-the-loop control, and multi-agent orchestration. The course centers on LangGraph as the primary framework for building production-grade agents, paired with a rigorous grounding in the concepts that make agentic architectures work — and a capstone track where you ship a real system.