Java Meets AI - A Hands On Guide to Building LLM Powered Applications with LangChain4j

Overview

Discover the transformative potential of integrating AI and Large Language Models in Java applications through a hands-on guide tailored for developers. Utilizing the LangChain4j framework, this course provides the tools to build robust, AI-powered solutions.

Benefit from practical insights and real-world applications designed to enhance your programming capabilities. Ideal for anyone seeking to expand their knowledge in AI integration within Java, this course is available on YouTube under the Artificial Intelligence Courses and Conference Talks categories.

Syllabus

• Introduction to AI and Large Language Models
Overview of AI and its applications
Understanding Large Language Models and their capabilities
• Setting Up the Development Environment
Installing Java and necessary tools
Introduction to LangChain4j
Setting up a Java project for AI integration
• Basics of LangChain4j
Understanding the LangChain4j architecture
Key components and interfaces
Writing a simple LangChain4j application
• Integrating Large Language Models in Java
Choosing and accessing language models
Using LangChain4j to connect with language models
Sending queries and processing responses
• Building AI-Powered Java Applications
Designing application workflows with LangChain4j
Handling model inputs and outputs effectively
Error handling and debugging best practices
• Case Study: Developing a Chatbot
Designing a conversational flow
Implementing chat functionality using LangChain4j
Enhancing responses with contextual awareness
• Advanced LangChain4j Features
Utilizing advanced APIs and customization
Integrating additional AI services and tools
Performance tuning and optimization techniques
• Security and Ethical Considerations
Understanding data privacy and security in AI applications
Addressing biases and ethical concerns
Implementing responsible AI practices
• Testing and Deployment
Writing test cases for AI-driven features
Continuous integration and deployment strategies
Monitoring and maintenance of AI applications
• Future Trends and Developments in AI and LangChain4j
Emerging technologies and innovations in AI
Expanding capabilities of Large Language Models
Future directions for LangChain4j
• Course Conclusion and Next Steps
Recap of key learning points
Resources for further learning and development
Opportunities for real-world application and projects

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Subjects

Conference Talks