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Java AI Foundations Conversational Apps with LangChain4j

Name: Java AI Foundations Conversational Apps with LangChain4j
Author: Baturi

Master stateless chatbots, ChatMemory, and AiServices for professional enterprise-grade Java LLM integration.
What you'll learn
✓ Implement persistent conversational context in stateless LLMs using the ChatMemory abstraction.
✓ Design reusable and testable Prompt Templates to reduce code complexity and manual string manipulation.
✓ Master LangChain4j's AiService to decouple AI behavior from boilerplate Java code and logic.
✓ Configure multi-user environments with secure, isolated session-based memory using unique MemoryIDs.
Requirements
● Intermediate proficiency in Java programming and IntelliJ IDEA.
● Basic understanding of LLM concepts (statelessness, prompts, and tokens).
● A valid API key for a major LLM provider (e.g., OpenAI or Anthropic).
Description
This course contains the use of artificial intelligence.
In the competitive landscape of 2025, mastering Conversational AI is no longer optional for Java developers. This course, "Java AI Foundations: Conversational Apps with LangChain4j," provides a strategic deep dive into building persistent, state-aware AI applications. While Large Language Models (LLMs) are inherently stateless—treating every request as independent—professional applications require a coherent dialogue history. You will learn how to bridge this gap using the ChatMemory and AiService abstractions.
We begin by establishing a foundational understanding of the Agentic Loop and the "Law of Complexity," where scalability is growth divided by complexity. You will learn to eliminate "boilerplate" code by moving away from hard-coded string literals and implementing a library of reusable Prompt Templates. This approach not only speeds up development but also ensures consistency and testability across your enterprise environment.
A major focus is placed on Enterprise Security and Privacy. You will implement multi-user architectures that isolate conversational contexts using MemoryIDs, ensuring that data from one user session never leaks into another. Finally, we explore advanced asynchronous patterns using CompletableFuture, allowing your applications to handle high-latency LLM calls without blocking critical system resources. By the end of this course, you will have the skills to architect and deploy a robust conversational bot tailored for complex business workflows.
Who this course is for
■ Java Developers tasked with integrating LLM capabilities into existing enterprise software.
■ Software Architects focused on reducing system complexity while scaling AI-powered features.
■ Data Professionals seeking professional Java-based alternatives to Python for conversational AI.
Homepage

https://www.udemy.com/course/java-ai-foundations-conversational-apps-with-langchain4j/

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Java AI Foundations Conversational Apps with LangChain4j

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