Java Meets AI - How to Build LLM-Powered Applications with LangChain4j

Overview

Learn to create AI-powered applications using Java, Spring Boot, and LangChain4j. Build chatbots, process unstructured data, and automate tasks with LLMs.

Explore key components and best practices for developing efficient, personalized AI solutions.

Syllabus

• Introduction to LangChain4j and LLMs
Overview of Large Language Models (LLMs)
Introduction to LangChain4j and its use with Java
Setting up the development environment: Java, Spring Boot, and LangChain4j
• Building AI-Powered Applications with Java
Integrating LLMs into Java applications
Designing scalable AI architecture with Spring Boot
Best practices for AI application development
• Developing Chatbots with LangChain4j
Basics of conversational AI and chatbots
Building a simple chatbot with LangChain4j
Enhancing chatbot capabilities with LLMs
• Processing Unstructured Data
Understanding unstructured data and its challenges
Techniques for processing text, images, and other unstructured data with LLMs
Implementing real-world data processing solutions
• Automating Tasks with AI
Identifying tasks suitable for LLM automation
Developing automated workflows with LangChain4j
Case studies and real-world examples
• Personalizing AI Solutions
Introduction to personalization in AI
Leveraging user data for personalizing applications
Best practices in privacy and personalization
• Key Components and Best Practices
Understanding core components of LangChain4j
Optimizing performance and efficiency
Testing and deployment of LangChain4j applications
• Capstone Project
Designing and building a fully functional AI-powered application
Integration of learned concepts
Presentation and critique of final projects
• Future Trends and Considerations
Emerging trends in AI and LLM development
Ethical considerations and future challenges
Continuing learning and resources in AI and Java development

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Subjects

Conference Talks