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Microsoft's New Java & AI Video Series Helps Devs Get Started with GenAI
Microsoft announced a new educational initiative called "Java and AI for Beginners: A Practical Video Series for Java," offering a structured introduction for Java developers interested in generative AI.
The series, published on the Microsoft for Java Developers blog, provides hands-on demonstrations that walk through the key components needed to integrate large language models and AI workflows into Java-based applications.
The video series begins by helping developers get started quickly with an AI-enabled Java app running in GitHub Codespaces. Viewers are guided through creating a new project, connecting to a language model, and generating text completions or chat-based responses. Early episodes also explain how function calling allows Java applications to interact with external services and APIs, enabling developers to extend AI functionality beyond text output and into practical automation scenarios.
Microsoft said the lessons progress from basic concepts to more complex topics that demonstrate how AI can be applied in production environments. One of the central themes of the series is retrieval-augmented generation (RAG), which combines vector-based search with AI-generated responses. This technique lets developers feed custom data into their AI models so applications can generate context-aware answers based on organization-specific content.
Later videos in the series cover multimodal generation, showing how text and image generation can be combined to create more interactive applications. Developers also learn how to run models both locally and in the cloud using Azure AI Foundry. The series introduces the Model Context Protocol (MCP), a framework that allows AI agents and services to share contextual information securely across connected tools. MCP support helps Java developers orchestrate workflows that involve multiple AI components running in different environments.
The tutorials incorporate several open-source libraries commonly used in the Java ecosystem. Developers learn to use LangChain4j, a Java library inspired by the LangChain framework, to connect prompts, models, and tools into reusable AI workflows. The series also demonstrates how to use the OpenAI Java SDK and Spring AI to streamline model integration and manage inference calls in scalable systems. Each lesson includes code samples that can be run directly in GitHub Codespaces or imported into local development environments.
Responsible AI development is another focus throughout the series. Microsoft provides examples showing how to apply best practices for content filtering, bias detection, and model safety. Developers are encouraged to validate model output, perform test coverage for AI-assisted logic, and apply ethical review processes before deployment. These lessons align with Microsoft's responsible AI principles while addressing the specific needs of Java teams building enterprise applications.
According to Microsoft, the goal of the series is to make generative AI accessible to all Java developers, regardless of prior AI experience. Each episode is designed to be concise, practical, and focused on one concept at a time, helping developers build confidence while gaining hands-on experience, Microsoft said. The lessons can be viewed in sequence for structured learning or individually for targeted reference when implementing AI features in existing projects.
All code samples, learning modules, and video tutorials are available on the post. The series serves as an entry point into Java-based generative AI development, providing examples that can be extended to production-grade solutions using Microsoft's cloud ecosystem.
About the Author
David Ramel is an editor and writer at Converge 360.