Generative AI and LLMs Training Course - Understanding, Customising and Deploying

A large language model (LLM) is a deep neural network — typically a transformer with billions to trillions of parameters — trained on vast text corpora to predict the next token in a sequence. This simple objective produces impressive emergent capabilities: dialogue, reasoning, summarization, translation, and code generation. The most prominent LLMs in 2026 are Claude, GPT, Gemini, and Llama. MFE-IT trains professionals on understanding, deploying, and integrating LLMs into business applications.

LLM training has three main stages: pre-training (predicting tokens on massive web-scale text), supervised fine-tuning on curated examples, and reinforcement learning from human feedback (RLHF) or AI feedback (RLAIF) to align outputs with helpful, honest, harmless behavior. Pre-training is the most compute-intensive, often taking months on thousands of GPUs. The MFE-IT LLMs training covers each stage and the implications for using and customizing LLMs.

Prompt engineering is the practice of crafting inputs to LLMs that produce reliable, high-quality outputs. Techniques include clear instructions, few-shot examples, structured formats (JSON, XML), chain-of-thought reasoning, role priming, and constraints on output length and style. Good prompts can dramatically improve results without changing the underlying model. Through MFE-IT’s hands-on approach, learners develop a robust prompt engineering toolkit applicable to any LLM.

AI is the broad field of systems that perform tasks normally requiring human intelligence. LLMs are one specific class of AI models — focused on language. Other AI types include computer vision, speech recognition, recommender systems, and reinforcement learning agents. Our MFE-IT training course on LLMs places them precisely within the broader AI landscape so participants understand their strengths and limits.