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GenAI for Busy Java Developers

Published by Pearson

Beginner to intermediate content levelBeginner to intermediate

GenAI Fundamentals to Supercharge Your Productivity and Accelerate Your Career Growth

  • Learn the fundamentals of AI/ML and understand how to effectively apply them.
  • Recognize why AI matters to professional Java developers and their careers.
  • Engage in hands-on exercises during class to build practical skills for real-world applications.

As a Java developer, you are probably knee-deep in handling production Java deployments and don’t have time to learn about GenAI. But your managers are talking more about AI every day, and you’re reading unsettling posts about how GenAI will take your job. In this session, we’ll take a realistic fast-track through AI and Machine Learning with a heavy slant towards Java developers who need to get the job done without the need to drill down into data science and complicated math. We’ll cover all the basics and explain how you can use AI in the software development process beyond simple code generation. You’ll gain a clear understanding of where AI is applicable (and where it isn’t), explore the ethical considerations involved, and learn how to boost your productivity and advance your career.

What you’ll learn and how you can apply it

  • Learn how Machine Learning and GenAI fundamentally work so you understand how to apply them.
  • Understand the difference between LLMs and traditional search engines
  • Apply context to improve LLM responses
  • Learn about recent trends in the GenAI world to be prepared

This live event is for you because...

  • You are a beginner/intermediate Java developer who is busy handling production systems and needs to get up to speed on the latest developments in GenAI
  • You are an application developer and not a data scientist who needs to incorporate GenAI features into your systems
  • You are concerned GenAI will eliminate your job and don’t know what direction to take my career

Prerequisites

  • Basic knowledge of Java
  • Some experience with deploying production systems
  • Basic knowledge of REST APIs
  • Access to the Internet

Course Set-up

  • Latest LTS of Java is recommended
  • IDE (preferably IntelliJ but any IDE will do)
  • Access to course Github Repo
  • API key to OpenAI, Google Gemini, or similar (we’ll use OpenAI in the labs, but any high-quality LLM will work)
  • Docker (recommended but not required)

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1: Origins and Basics [30 min]

  • Patterns and Machine Learning
  • Neural Networks and Weights
  • GenAI vs PredAI
  • NLP and Language Models
  • Quiz: 5 quiz questions
  • Q&A (5 minutes)
  • Break (5 minutes)

Segment 2: Prompts, Context [40 min]

  • Prompt “Engineering” Techniques
  • Zero-shot, N-shot, Chain-of-Thought
  • The Importance of Context

Quiz: 5 quiz questions

  • Exercise: Test various prompt techniques with a chatbot
  • Q&A (5 minutes)
  • Break (5 minutes)

Segment 3: Programmatic Access to GenAI Services [30 min]

  • REST interfaces - OpenAI, Google, etc
  • Java APIs
  • Langchain4j
  • OpenAI services

Quiz: 5 quiz questions

  • Exercise: Write a simple Java program that accesses an LLM
  • Q&A (5 minutes)
  • Break (5 minutes)

Segment 4: Chatbot Architecture [40 min]

  • System, User, and Assistant Messages
  • Chatbot Architecture
  • User context
  • Assistant context

Quiz: 5 quiz questions

  • Exercise: Write a simple Java chatbot
  • Q&A (5 minutes)
  • Break (5 minutes)

Segment 5: Chatbots, RAG, and Embeddings [40 min]

  • Understand my documents
  • Retrieval Augmented Generation (RAG)
  • Embedding Vectors and Similarity

Quiz: 5 quiz questions

  • Exercise: Write an app that determines the similarity between two strings
  • Q&A (5 minutes)

Next steps and Course wrap-up (5 minutes)

Your Instructor

  • Frank Greco

    Frank is a long-time denizen of the local NY tech scene and global Java community, a senior consultant and enterprise architect focusing on AI / Machine Learning, Cloud, and Mobile/Edge computing. More than just a typical technology consultant, Frank is a long-time educator, a prolific writer, a developer community builder, a mentor, and an expert on tech partnerships and innovations, especially in financial systems and enterprise computing.

    Frank is the co-author of JSR #381 "VisRec", a Java API for visual recognition and machine learning. He often shares his insights at top tech conferences worldwide, such as DevNexus, Dev2next, QCon, Jfokus, Devoxx, IDEA Conf, TechTran, JavaOne, etc.

    In addition, Frank is a recognized Java Champion and the founder/Chairman of NYJavaSIG, the world’s first and North America’s largest Java User Group. As a long-time musician, Frank wrote and performed “Java Jam” with the band The Yield, the first song about the Java Programming Language, at The Bitter End in 1996.

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Skill covered

Java