AI for Tech
Use LLMs to help you code and create intelligent applications
Disclaimer
LLMs and Multimodal LLMs are constantly evolving, with new developer-oriented tools and practices continually emerging.
Summary
Module 1: Mastering LLMs in the SDLC
a. Introduction
Key concepts and terminology — History — Market overview — Use cases in software development
b. Prompting in the app lifecycle
Prompting techniques — Ideation, architecture, refactoring and test generation with LLMs
c. Online/Offline LLM clients
LibreChat, LM Studio — Presets, RAG, plugins, offline model configuration
d. Code Assistants in IDEs
GitHub Copilot — Spec Driven Development (Bolt, V0, GitHub Spark)
e. Agentic CLIs
Claude Code, GitHub Copilot CLI, Gemini CLI — AGENTS.md standard — Skills — MCP client
Module 2: Building Intelligent Apps
a. GenAI for services
LLM REST API — LangChain (prompt templates, chaining, tool calling) — llama-index RAG — Embeddings — Exercises 01–07
b. Agentic AI for services
LangChain agents — Multi-agent orchestration — MCP servers with FastMCP — 3 Colab exercises (08–10)
c. Node-based tools
N8N — Visual agentic orchestration — No-code AI workflows
d. Cloud tools
Vertex AI — Google Colab — Cloud AI APIs (Text-to-Speech, Translation, etc.)
About
We design payments technology that powers the growth of millions of businesses around the world. Engineering the next frontiers in payments technology
- European leader in payment and secured transactions. Over 50bn transactions/year
- 7000+ engineers in over 40 countries
Contributors
Ibrahim Gharbi · Sylvain Pollet Villard · Yassine Benabbas · Raphaël Semeteys
Sponsors
Yacine Kessaci · Liyun He Guelton · Fanilo Andrianasolo · Vijayanand Premnath · Vincent Caquelard · Mat Goodger · Effan Mutembo · Cyril Cauchois · Martin Boulanger · Julien Carme