Context Engineering Workshop for Java Developers

Overview

Welcome to this hands-on workshop, where you'll learn to implement sophisticated context-engineering patterns. Context Engineering is the practice of strategically designing, structuring, and optimizing the information provided to AI models (particularly LLMs) to achieve desired outputs. It goes beyond simple prompt engineering by considering the entire context window and how data is organized, presented, and sequenced to maximize model performance. In this workshop, you will learn how to implement this using Java, LangChain4J, and Redis.

🤔 Why Context Engineering?

LLMs have limited context windows and no inherent memory between conversations. Without proper context engineering:

• Models lose track of conversation history
• Responses lack relevant domain knowledge
• Token limits are exceeded, causing failures
• Repeated API calls increase costs

This workshop teaches you patterns to solve these challenges systematically.

🎯 What you'll build

By the end of this workshop, you'll have built a complete AI application featuring:

• LLM integration using OpenAI, Spring Boot, and LangChain4J
• Vector embeddings for semantic search with a chunking strategy
• RAG (Retrieval-Augmented Generation) with knowledge bases
• Dual-layer memory architecture (short-term and long-term memory)
• Query compression techniques for efficient context retrieval
• Content Reranking to optimize the relevance of retrieved information
• Few-shot learning pattern for improved generated responses
• Dynamic context window management based on token limits
• Semantic caching to optimize performance and reduce LLM costs

📋 Prerequisites

Required knowledge

• Basic understanding of Java programming
• Basic knowledge of LLMs and AI concepts
• Familiarity with command-line interfaces
• Basic understanding of Docker and Git
• Familiarity with RESTful APIs

Required software

• Java 21+
• Maven 3.9+
• Docker
• Git
• Node.js

Required accounts

Account	Description	Cost
OpenAI	LLM that will power the responses for the AI application	Free trial sufficient
Redis Cloud	Semantic caching service powered by Redis LangCache	Free tier sufficient

💡 Note on OpenAI costs: While OpenAI occasionally offers free trial credits to new accounts, this workshop assumes pay-as-you-go pricing. The estimated cost of $1-3 covers all 9 labs. You can monitor your usage in the OpenAI dashboard to track costs in real-time.

🗺️ Workshop Structure

This workshop has an estimated duration of 2 hours and is organized into 9 progressive labs, each building on the previous one. Each lab introduces a specific context engineering challenge, which is then addressed in the subsequent lab.

Lab	Topic	Duration	Branch
1	Set up and deploy the AI application	25 mins	lab-1-starter
2	Enabling short-term memory with chat memory	10 mins	lab-2-starter
3	Knowledge base with embeddings, parsers, and splitters	10 mins	lab-3-starter
4	Implementing basic RAG with knowledge base data	15 mins	lab-4-starter
5	Enabling on-demand context management for memories	10 mins	lab-5-starter
6	Implementing query compression and context reranking	15 mins	lab-6-starter
7	Implementing a few-shot into the system prompt design	05 mins	lab-7-starter
8	Enabling token management to handle token limits	05 mins	lab-8-starter
9	Implementing semantic caching for conversations	25 mins	lab-9-starter

Each lab also contains a corresponding lab-X-solution branch with the completed code for reference. You can use this branch to compare your current implementation using git diff {lab-X-solution}. Alternatively, you can switch to the solution branch at any time during the lab if you are falling behind or to get unstuck.

🚀 Getting Started

Step 1: Clone the repository

git clone https://github.com/redis-developer/context-engineering-workshop-java.git

Step 2: Verify your Environment

Ensure you have Java, Maven, Node.js, Docker, and Git installed. You can check their versions with:

java -version
mvn -version
npm --version
docker --version
git --version

Step 3: Begin your First Lab

Navigate to the cloned repository.

cd context-engineering-workshop-java

Click on the link for Lab 1: Set up and deploy the AI application to get started!

📚 Resources

• Alexa Skill using Context Engineering
• LangChain4J Documentation
• Redis Agent Memory Server
• Redis LangCache Documentation
• OpenAI API Reference

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

👥 Maintainers

• Ricardo Ferreira — @riferrei

📄 License

This project is licensed under the MIT License.