This course is designed to help participants confidently understand and evaluate the outputs of modern AI systems. The course focuses on interpretation rather than model training, enabling delegates to assess how AI and AGI models generate results, recognise limitations and failure modes, and determine when outputs can be trusted for operational or business use. Participants explore how large language models and AUTO-GPT–style agent systems produce and chain responses, how context and assumptions influence outcomes, and how to validate results effectively. The course also examines vector databases, explaining how embeddings, similarity search, and semantic retrieval work in practice, and how to interpret relevance scores and analytical outputs.
Through real-world examples and hands-on exercises, delegates gain practical experience using LLMs alongside tools such as Pandas to analyse data and extract meaningful insight.
Pre-requisites: A basic appreciation of AI technology is assumed.
