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Each participant will work with real LLM tools and APIs. You will build real use cases: chatbot, document summariser, domain-specific assistant. This training course is regularly updated to keep pace with the rapid developments in the field.
This training course is part of our Artificial Intelligence Training Courses. Explore our other AI training courses to fully leverage machine learning, LLMs and generative AI.
To deepen your skills, also explore our LLMs training course and our n8n automation and AI training, both perfectly complementing this Generative AI program.
By the end of this training course, each participant will be able to:
This training course is designed for :
Generative AI refers to artificial intelligence systems that produce new content — text, images, code, audio, video — based on patterns learned from training data. The most prominent examples are large language models (GPT-4, Claude, Gemini, Llama) and image generators (DALL·E, Midjourney, Stable Diffusion). It powers chatbots, code assistants, content creation, and intelligent automation. MFE-IT trains professionals on building applications that leverage generative AI safely and effectively.
AI is the broader field encompassing any system that performs tasks normally requiring human intelligence — classification, prediction, optimization, perception. Generative AI is a subset focused specifically on creating new content rather than analyzing or classifying existing data. The MFE-IT generative AI training distinguishes both clearly so participants pick the right tool for each business problem.
Large language models are neural networks (typically transformer-based) trained on massive text corpora to predict the next token in a sequence. Through this training they implicitly learn grammar, facts, reasoning patterns, and stylistic conventions. At inference, they generate text by sampling from probability distributions over tokens. Through MFE-IT’s hands-on approach, learners explore tokenization, embeddings, attention, and prompt engineering with real LLMs.
In 2027, the leading frontier LLMs are Claude 4.7 (Anthropic), GPT-5 (OpenAI), Gemini 2.5 (Google), and Llama 4 (Meta) for open-weight models. Each excels at different tasks: long-context reasoning, code, multimodal input, or on-device deployment. Our MFE-IT training course on generative AI and LLMs benchmarks current models for typical enterprise use cases and shows how to choose the right one.