Development with LLMs and Agents Trainings

Why LLM Development is a Critical Skill for Developers in 2026

Large language models (LLMs) have transformed software development. In 2026, knowing how to call an OpenAI API, design a RAG architecture or orchestrate a multi-agent system is no longer a niche speciality: it is a skill expected in many IT projects. Companies are investing heavily in AI applications, and demand for developers capable of working with LLMs far outstrips available supply.

These LLM development training courses are aimed at Python or backend developers who want to acquire practical skills for building AI applications in production. Unlike general AI training, they focus on the code, APIs, architectures and patterns you will actually use in your projects. Each course is structured around practical labs with real code, real problems and directly reusable solutions.

AI Development with Python: the Technical Foundation

The training Developing AI Applications with Python is the recommended starting point for any developer wishing to build LLM-based applications. It covers the fundamentals: prompt and context management, API calls, response streaming, token handling, error management and integration into web applications or services.

OpenAI API: GPT, Assistants and RAG

OpenAI remains the reference platform for many AI projects. The training Developing with the OpenAI API: GPT, Assistants and RAG covers the entire OpenAI ecosystem: completion API, Assistants API with threads and runs, function calling, long context management and implementation of RAG patterns with OpenAI embeddings. Practical labs allow you to build a complete assistant with memory and access to proprietary data.

Claude API, Gemini and AWS Bedrock: the Other Major Platforms

Claude is recognised for its step-by-step reasoning capabilities. The training Developing with the Claude API and Anthropic Platform covers the Anthropic API, Claude’s specific features (vision, tool use), system prompt management and implementing safety guardrails. The training Developing with Gemini and Vertex AI covers Gemini models on Google Cloud and multimodal capabilities. The training Developing with AWS Bedrock: Applications and Agents covers access to foundation models (Claude, Llama, Mistral) on Amazon infrastructure.

LangChain and RAG: Building Retrieval-Augmented Applications

LangChain has become the reference framework for building complex LLM applications in Python. The training Developing LLM and RAG Applications with LangChain covers processing chains, LangChain agents, integration of vector databases (Chroma, Pinecone, Weaviate), document chunking and indexing, and the implementation of a complete RAG pipeline enabling your applications to answer questions on proprietary data.

Multi-Agent Architecture: Designing Collaborative AI Systems

The training Designing Multi-Agent AI Architecture covers coordination patterns between agents (orchestration, parallelism, specialisation), frameworks such as LangGraph and AutoGen, shared state and context management, and testing and monitoring strategies for multi-agent systems. This is the most advanced training in this domain, aimed at experienced developers.

GitHub Copilot and Azure Semantic Kernel

For developers wishing to boost their coding productivity, the training GitHub Copilot (GH-300) covers using GitHub Copilot in VS Code, prompting techniques for generating quality code and best practices for integrating Copilot into a professional development workflow. The training Develop AI Agents Using Azure OpenAI and the Semantic Kernel SDK (AZ-2005) covers creating AI applications and agents with Azure OpenAI Service and Semantic Kernel. The training Accelerate App Development by Using GitHub Copilot (AZ-2007) covers AI-assisted development with GitHub Copilot on Azure projects.

Links with Other Domains for Developers

For developers working specifically on the Microsoft Azure ecosystem, our Azure AI Platforms domain covers Azure AI cognitive services and the AI-102 certification. For creating AI agents in the Microsoft 365 ecosystem, explore our training in Copilot Studio and Microsoft Agents. For data science and machine learning aspects, our Data Science and Applied AI domain covers TensorFlow, machine learning and Azure Databricks solutions.

LLM Development Training in Geneva, Lausanne and Virtual Classroom

These two-day training courses are delivered by AI expert developers who build and deploy LLM applications in production. Sessions take place in person in Geneva and Lausanne or as virtual classroom sessions. They are eligible for Temptraining funding for employees residing or working in Switzerland.