Generative-AI-for-beginners-java
Learn Generative AI fundamentals through Java programming.
Stars: 149
Generative AI for Beginners - Java Edition is a comprehensive workshop that introduces users to the core concepts of generative AI, Java AI ecosystem, model context protocol, and practical applications. The course covers topics such as prompt engineering, embeddings & vector operations, retrieval-augmented generation, and responsible AI development. Users can explore real-world scenarios like chatbots, content generation, and ethical AI development. The workshop provides a hands-on learning experience with multi-language support and development environment setup guidance.
README:
Time Commitment: The entire workshop can be completed online without local setup. The environment setup takes 2 minutes, with exploring the samples requiring 1-3 hours depending on exploration depth.
Quick Start
- Fork this repository to your GitHub account
- Click Code → Codespaces tab → ... → New with options...
- Use the defaults – this will select the Development container created for this course
- Click Create codespace
- Wait ~2 minutes for the environment to be ready
- Jump straight to The first example
French | Spanish | German | Russian | Arabic | Persian (Farsi) | Urdu | Chinese (Simplified) | Chinese (Traditional, Macau) | Chinese (Traditional, Hong Kong) | Chinese (Traditional, Taiwan) | Japanese | Korean | Hindi | Bengali | Marathi | Nepali | Punjabi (Gurmukhi) | Portuguese (Portugal) | Portuguese (Brazil) | Italian | Polish | Turkish | Greek | Thai | Swedish | Danish | Norwegian | Finnish | Dutch | Hebrew | Vietnamese | Indonesian | Malay | Tagalog (Filipino) | Swahili | Hungarian | Czech | Slovak | Romanian | Bulgarian | Serbian (Cyrillic) | Croatian | Slovenian | Ukrainian | Burmese (Myanmar)
- Core Concepts: Understanding Large Language Models, tokens, embeddings, and AI capabilities
- Java AI Ecosystem: Overview of Spring AI and OpenAI SDKs
- Model Context Protocol: Introduction to MCP and its role in AI agent communication
- Practical Applications: Real-world scenarios including chatbots and content generation
- → Start Chapter 1
- Multi-Provider Configuration: Set up GitHub Models, Azure OpenAI, and OpenAI Java SDK integrations
- Spring Boot + Spring AI: Best practices for enterprise AI application development
- GitHub Models: Free AI model access for prototyping and learning (no credit card required)
- Development Tools: Docker containers, VS Code, and GitHub Codespaces configuration
- → Start Chapter 2
- Prompt Engineering: Techniques for optimal AI model responses
- Embeddings & Vector Operations: Implement semantic search and similarity matching
- Retrieval-Augmented Generation (RAG): Combine AI with your own data sources
- Function Calling: Extend AI capabilities with custom tools and plugins
- → Start Chapter 3
-
Pet Story Generator (
petstory/): Creative content generation with GitHub Models -
Foundry Local Demo (
foundrylocal/): Local AI model integration with OpenAI Java SDK -
MCP Calculator Service (
calculator/): Basic Model Context Protocol implementation with Spring AI - → Start Chapter 4
- GitHub Models Safety: Test built-in content filtering and safety mechanisms (hard blocks and soft refusals)
- Responsible AI Demo: Hands-on example showing how modern AI safety systems work in practice
- Best Practices: Essential guidelines for ethical AI development and deployment
- → Start Chapter 5
- MCP For Beginners
- AI Agents For Beginners
- Generative AI for Beginners using .NET
- Generative AI for Beginners using JavaScript
- Generative AI for Beginners
- ML for Beginners
- Data Science for Beginners
- AI for Beginners
- Cybersecurity for Beginners
- Web Dev for Beginners
- IoT for Beginners
- XR Development for Beginners
- Mastering GitHub Copilot for AI Paired Programming
- Mastering GitHub Copilot for C#/.NET Developers
- Choose Your Own Copilot Adventure
- RAG Chat App with Azure AI Services
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