GENAI200DEVAUGMENTE

AI-Augmented Developer

Accelerate the productivity of your development teams with Agentic Coding. In a market where development speed and code quality make the difference, this training transforms your developers into “augmented developers,” capable of leveraging the most advanced AI agents. Focused on real-world use cases, the training emphasizes immediate value creation and concrete improvement in developer productivity. Participants leave with methods, workflows, and assets directly applicable to their corporate projects.

✓ Formation officielle SFEIR InstituteNiveau Intermediate⏱️ 2 jours (14h)

Ce que vous allez apprendre

Accelerate all phases of the development cycle, from architectural design to resolving complex bugs, increasing their velocity while ensuring code quality, security, testing, documentation, and maintainability.
Collaborate continuously with AI by adopting AI-augmented work methods that enhance productivity and efficiency.
Master the ecosystem of leading tools for coding with AI and AI frameworks (like Claude Code, Gemini CLI, GitHub Copilot, Cursor AI), evaluate and adopt new emerging tools, understanding their respective strengths and limitations.
Transform a classic team into an augmented team by orchestrating collaborative AI agents, establishing team standards (shared contexts, reusable prompts), and disseminating best practices for AI-assisted development that accelerate onboarding and standardize workflows.

Prérequis

Practical mastery of at least one programming language (Python, JavaScript, Java, C#, TypeScript, Go...).
Daily experience with Git and the use of a modern IDE (VS Code, IntelliJ, WebStorm, etc.).
Possess basic command-line and file editing skills.
Teamwork experience: code review, collaborative workflows.
A sensitivity to generative AI and prompt engineering is an asset to maximize the benefits of the training.
A standard laptop (16 GB recommended) with rights to install software.
Recent operating system (Windows 10+, macOS 10.15+, Linux).
Git installed and configured with access to GitHub/GitLab.
Recent version of NodeJS and npm installed and configured.
An IDE of choice (VS Code, IntelliJ, WebStorm...).
A stable internet connection.
Docker (optional but highly recommended to benefit from an automated setup via our devcontainer).

Public cible

Software Engineer (operational backend/frontend developers), Software Architect, Tech Leader in companies, IT services companies, startups, and scale-ups who want to boost their efficiency with AI while maintaining a high level of code quality., Teams concerned with maintainability, robustness, and best practices, looking to use AI to modernize their methods while improving the quality of their deliverables.

Programme de la Formation

8 modules pour maîtriser les fondamentaux

Sujets abordés

→AI Refresher: Quick review, history of AI / ML / NLP / Generative AI, arrival of ChatGPT/LLM
→Market Models: GPT-5, Claude, Gemini - key differences
→The Art of Prompting: how to communicate effectively with AI to get quality code
→Evolution of tools: from ad-hoc assistance to collaborative agents
→Context and tokens: understanding the constraints
→Overview of tools: ChatGPT, Claude, etc.

Activités

Prompt engineering: comparison of 3 approaches on a concrete case

Quality Process

SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach

Teaching Methods Used

Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
Guided Labs — Guided practical exercises on software, hardware, or technical environments.

Evaluation and Monitoring System

The achievement of training objectives is evaluated at multiple levels to ensure quality:

Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.