Skip to content

worldline/learning-ai

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

97 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

From Generative to Agentic AI for Developers

Description

This course is designed for developers aiming to harness the capabilities of generative artificial intelligence in their software development processes and their application services. It provides a comprehensive overview of generative AI, covering its significance, foundational concepts, and practical applications.

The training will explore prompting frameworks to enhance the application lifecycle, engage with essential AI tools and plugins, and learn to utilize code assistants within integrated development environments (IDEs) for generating code and complete websites.

It also addresses the management of API calls to large language models (LLMs), the use of context-aware frameworks, and the deployment of AI solutions using cloud provider tools such as Google Cloud's Vertex AI and Colab notebooks.

By the end of the course, participants will be equipped with the knowledge and skills necessary to effectively integrate generative AI into their development workflows, fostering innovation and efficiency.

Syllabus

Module 1: Introduction to Generative AI

  • Overview of Generative AI and its significance
  • Key concepts and terminology
  • Use cases in software development

Module 2: Prompting usage in apps lifecycle

  • Understanding prompting techniques
  • Tips for ideation and architecture design
  • Refactoring and generating tests using AI

Module 3: Online/Offline LLMs clients

  • Overview of main plugins and their functionalities
  • Mixing plugins and using presets
  • Introduction to Retrieval-Augmented Generation (RAG)

Module 4: Code Assistants in IDEs

  • Features of code assistants
  • Generating code snippets and tests
  • Creating a website from prompts

Module 5: GenAI for services

  • Managing LLM API calls in JSON mode and handling structured outputs
  • Use context-aware frameworks with techniques for prompt templating and chaining
  • Usage of vector databases for chain-of-thought creations
  • Retrieval-Augmented Generation (RAG) structured and unstructured.

Module 6: Agentic AI with MCP (kotlin-mcp-sdk)

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

Module 7: GenAI with UI node-based tools

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

Module 8: Cloud Provider Tools

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

Module 7: Agentic AI with MCP framework (soon)

AI for devs | Tech at Worldline

Who we are ?

avatar

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

  • Leader in payment and secured transactions.
  • Over 50bn transactions/year
  • 7000+ engineers in over 40 countries
  • A huge & diverse tech-stack

Follow trainers of this codelab

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

Follow our Tech team

🔗 blog.worldline.tech 🔗 @WorldlineTech

Worldline © 2024 | Tech at Worldline

About

a GenAI training module

Resources

License

Apache-2.0, GPL-3.0 licenses found

Licenses found

Apache-2.0
LICENSE
GPL-3.0
LICENSE.txt

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published