Generative AI for Developers

use LLMs to help you code and create your applications

Introduction

Prerequisites - Key concepts and terminology - A bit of History - The market

Prompting Frameworks usage in apps lifecycle

Understanding prompting techniques, developper usage of LLMs for coding, and the application lifecycle

Online/Offline LLMs clients (Librechat & LM Studio)

History, Favorites, Presets, Preformatted prompts, AI providers, Agent, Plugins & mixing, RAG, Installation, Model configuration, enable APIs...

Code Assistants in IDEs (Copilot)

GitHub Copilot, and project generation with Bolt, v0

GenAI for services (langchain, llama-index)

LLM API calls, Developpement with LangChain, Python notebooks, Context aware frameworks, RAG, Vector databases, Agents, Model Context Protocol

Agentic AI with MCP (kotlin-mcp-sdk)

Understanding the Model Context Protocol, Building agentic AI applications, Integrating MCP with existing tools and workflows

GenAI with UI node-based tools (ComfyUI)

Creating and customizing AI workflows with ComfyUI, Integrating LLMs with ComfyUI, Building complex workflows with various AI models and techniques

GenAI in the Cloud (vertex AI)

Overview of Cloud tools (Vertex AI, Google Colab notebooks, etc.), Utilizing AI Cloud APIs (Text-to-Speech, Translation, etc.), deploying AI solutions in the cloud

Disclamer

LLMs and MMLLMs are constantly evolving, and new applications for developer training are emerging all the time.

AI for devs | Tech at Worldline

Who we are

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We design payments technology that powers the growth of millions of businesses around the world. Engineering the next frontiers in payments technology