Generative AI and LLMs Training Course – Design, Customise and Deploy Powerful Models
- Price
- Duration
- Number of hours
Each session will take place even if only one person is registered (except in cases of force majeure).
Description of the Generative AI and LLMs Training Course
Large Language Models (LLMs) such as GPT, Claude or Mistral are transforming how we interact with technology. But between their theoretical potential and real-world deployment, there is a significant gap. This training course bridges that gap: you will learn how these models work, how to customise them for your needs, and how to deploy them in real environments — from API integration to fine-tuning, through to RAG architectures.
Whether you are a developer, data scientist or IT architect, this training course will give you the technical tools to get the most out of LLMs in your projects.
Also discover our Microsoft AI-103 Azure AI Expert Training Course, our n8n and AI Training Course – Building Autonomous and Intelligent Workflows, our Azure AI Document Intelligence AI-3002 Training Course, our Azure AI-3003 Training Course – Natural Language Processing (NLP) Solutions, our Azure AI Vision AI-3004 Training Course: Computer Vision Solutions, our Generative AI with Azure Databricks Training and our Azure AI Agents AI-3026 Training Course.
Format
Remote (recorded sessions). It is possible to customise the training course for a private group. Contact us for more information.
GOOD TO KNOW
This training course includes numerous exercises (60% practical). 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.
Objectives of the Generative AI and LLMs Training Course
By the end of this training course, each participant will be able to:
- Understand how LLMs work (architecture, training, limitations)
- Choose the right model for a given use case (GPT, Claude, Mistral, open-source)
- Integrate a model via API (OpenAI, Anthropic, HuggingFace)
- Build a RAG (Retrieval-Augmented Generation) pipeline
- Fine-tune a model on custom data
- Deploy an LLM application in a real environment
- Evaluate model performance and manage costs
Prerequisites
- Python programming experience recommended
- Basic understanding of machine learning concepts
- Familiarity with APIs (REST)
- Because each participant is unique, a personalised interview with our expert allows us to design a training programme perfectly aligned with their objectives, level and professional challenges.
Target Audience
This training course is designed for :
- Python developers
- Data scientists and ML engineers
- IT architects wishing to integrate LLMs into their systems
- Technical profiles working on AI projects
Detailed Programme of the Generative AI and LLMs Training Course
How LLMs Work
How LLMs work (transformers, embeddings, tokens, generation models). Why do they produce what they produce? A simplified introduction.
Overview of Major Models
GPT-5, Claude, LLaMA, Mistral, Mixtral, Phi… how to choose based on your needs (cost, performance, privacy, open source, etc.). Comparative demos.
Prompt Engineering and RAG
Prompt engineering techniques: roles, contexts, loops, logical chains. Using memory, RAG (Retrieval-Augmented Generation) and agents.
Integrations and APIs
Using OpenAI, HuggingFace, Anthropic APIs. Creating web assistants, integration with Notion, Slack, CRMs, etc. Low-code or custom frameworks.
Deployment Use Cases
Deployment examples: internal chatbot, business copilot, meeting summarisation, legal or HR summary engine. Local or cloud deployment (serverless, etc.).
Governance, Security and Responsible AI
Governance, data security, hallucinations, output control, auditability. Responsible use cases and error management.
Why This Training Stands Out
What sets this training apart:
- It demystifies LLMs without oversimplifying them, with a balance between theory and practice.
- It goes beyond prompts to explore advanced techniques such as RAG, agents and fine-tuning.
- Real, immediately applicable use cases.
FAQ – Generative AI and LLMs Training
What is generative AI?
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.
What is the difference between AI and generative AI?
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.
How do LLMs work?
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.
What are the best LLMs in 2026?
In 2026, 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.
Would you like to know about upcoming sessions ?
Would you like to schedule this training course on a specific date ? Contact us by email or by filling out the contact form.