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Spring AI Tutorials: Build Real-World AI Applications with Spring Boot

Practical projects are the fastest way to internalize Spring AI concepts. While the Framework and RAG sections provide the architectural foundation, tutorials bridge the gap between understanding individual APIs and designing complete, production-oriented AI systems. This section offers hands-on implementation guides that walk you through building real applications—chatbots, knowledge bases, document Q&A, AI assistants, agents, and more—using Spring Boot and Spring AI.

Each tutorial is self-contained but cross-references relevant handbook sections so you can deepen your theoretical understanding as you code. The projects are designed to be progressively more complex, reflecting the journey from a single ChatClient call to an enterprise-ready AI platform.

Tutorials cover:

  • Chat applications
  • RAG systems
  • Knowledge bases
  • Document processing
  • AI assistants
  • AI agents
  • MCP integrations
  • Enterprise deployment

Spring AI Tutorial Roadmap​

The following progression guides you from a simple AI chat to a fully deployed, observable system. Each stage builds on the skills learned in the previous one.

Basic AI Chat Application
|
v
Prompt Engineering
|
v
RAG Application
|
v
Enterprise Knowledge Base
|
v
AI Assistant
|
v
AI Agent
|
v
Production Deployment
  • Basic AI Chat Application – Connect to a model and send your first prompt.
  • Prompt Engineering – Structure prompts, use templates, and control model behavior.
  • RAG Application – Ground responses in your own documents using retrieval-augmented generation.
  • Enterprise Knowledge Base – Scale RAG with security, access control, and metadata.
  • AI Assistant – Add memory, context management, and tool integration.
  • AI Agent – Enable autonomous reasoning, tool calling, and multi-step workflows.
  • Production Deployment – Containerize, monitor, and harden your AI service for production.

Getting Started Tutorials​

These tutorials are for developers building their first Spring AI applications. They cover environment setup, basic chat interactions, and prompt engineering fundamentals.

Getting Started with Spring AI​

URL: /spring-ai/getting-started/getting-started/

A complete introduction that walks you through project setup, dependency management, provider configuration, and sending your first AI request. Start here if you have never used Spring AI before.

Build Your First Spring AI Chatbot​

URL: /spring-ai/tutorials/chatbot/

Create a conversational chatbot using ChatClient and ChatModel. Learn how to manage conversation flow, handle responses, and build a simple interactive loop. This is the natural next step after the Getting Started guide.

Spring AI Prompt Engineering Tutorial​

URL: /spring-ai/framework/prompt/

Master prompt design with PromptTemplate. Learn to create reusable, structured prompts, inject variables, and apply best practices that improve response quality and consistency.

RAG Application Tutorials​

Retrieval-Augmented Generation (RAG) is the most common enterprise Spring AI pattern. These tutorials teach you to build systems that answer questions from your own documents.

Build a Spring AI RAG Application​

URL: /spring-ai/tutorials/rag/

Build a complete RAG pipeline from scratch. This tutorial covers document loading, chunking strategies, embedding generation, vector search, and prompt augmentation. By the end, you will have a working RAG application that can retrieve and synthesize information.

Build an Enterprise Knowledge Base with Spring AI​

URL: /spring-ai/tutorials/knowledge-base/

Scale your RAG application into an enterprise knowledge base. Learn to handle diverse document types, design metadata schemas, enforce access control, and architect for production workloads. This tutorial connects RAG concepts with enterprise patterns.

→ Related sections: Spring AI RAG | Enterprise AI

Build a Document Q&A System​

URL: /spring-ai/tutorials/document-qa/

Focus on document processing: PDF ingestion, text extraction, and transformation. Build a Q&A system that allows users to ask questions against a set of uploaded documents, with source citations in the answers.

Vector Database Tutorials​

These tutorials provide practical guidance for integrating vector stores into your Spring AI applications.

Spring AI with PGVector​

URL: /spring-ai/vector-database/pgvector/

Use the PGVector extension for PostgreSQL as your vector store. Learn schema design, index creation, and how to perform similarity searches. Ideal for teams that already use PostgreSQL.

Spring AI with Milvus​

URL: /spring-ai/vector-database/milvus/

Deploy Milvus, a high-performance open-source vector database, and connect it to Spring AI. Covers collection creation, index tuning, and performance optimization for large-scale retrieval.

AI Provider Integration Tutorials​

Learn how to connect Spring AI applications with different large language model providers.

OpenAI Integration with Spring AI​

URL: /spring-ai/providers/openai/

Integrate with OpenAI GPT models. This tutorial covers API configuration, chat and embedding models, streaming responses, and function calling.

Azure OpenAI Enterprise Deployment​

URL: /spring-ai/providers/azure-openai/

Deploy Spring AI with Azure OpenAI using enterprise authentication, private networking, and Azure-specific security features. Understand the differences from the standard OpenAI integration.

Local AI with Ollama​

URL: /spring-ai/providers/ollama/

Run open-source models locally with Ollama. Perfect for development, testing, and air-gapped environments. Learn how to configure Spring AI to use local models without sending data to external services.

AI Assistant Tutorials​

Build application-level AI assistants that maintain context and integrate with tools.

Build an AI Assistant with Spring AI​

URL: /spring-ai/tutorials/ai-assistant/

Create a stateful AI assistant with conversation memory and context management. Integrate external tools to expand its capabilities, and learn to manage multi-turn interactions.

Build a Document Intelligence Assistant​

URL: /spring-ai/tutorials/document-assistant/

Combine RAG and assistant patterns to build a system that understands, retrieves, and reasons over enterprise documents. Integrate workflows for document review, summarization, and Q&A.

AI Agent Tutorials​

Move from reactive systems to autonomous AI agents that plan, reason, and act.

Build an AI Agent with Spring AI​

URL: /spring-ai/tutorials/agent/

Build an autonomous agent that uses tool calling, a reasoning loop, and memory to accomplish multi-step tasks. Learn how to define tools, manage the agent's state, and control its behavior.

Build an MCP Enabled AI Application​

URL: /spring-ai/tutorials/mcp/

Implement the Model Context Protocol to standardize how your AI application discovers and uses external tools. Build an MCP server, register tools, and create a client that dynamically invokes them.

Enterprise Spring AI Tutorials​

Prepare your Spring AI applications for production with deployment and monitoring tutorials.

Deploy Spring AI Application to Kubernetes​

URL: /spring-ai/tutorials/kubernetes/

Containerize your Spring AI service, manage configuration with ConfigMaps and Secrets, and deploy to Kubernetes. Learn about horizontal scaling, health checks, and resource management for AI workloads.

Production Monitoring for Spring AI​

URL: /spring-ai/enterprise/observability/

Set up tracing, metrics, and dashboards for your Spring AI applications. Monitor token usage, request latency, and error rates to ensure reliable production operation.

Different roles benefit from different sequences. Choose the path that aligns with your goals.

Path 1: Java Developer​

Ideal for developers new to AI who want to build practical applications quickly.

  1. Getting Started with Spring AI
  2. Build Your First Spring AI Chatbot
  3. Prompt Engineering Tutorial
  4. Build a Spring AI RAG Application

Path 2: Backend Engineer​

For engineers integrating AI into existing backend systems and infrastructure.

  1. Framework Architecture (in Framework section)
  2. AI Provider Integration Tutorials
  3. Vector Database Tutorials
  4. Build an Enterprise Knowledge Base

Path 3: AI Solution Architect​

For architects designing end-to-end AI platforms and production systems.

  1. RAG Architecture (in RAG section)
  2. AI Agent Tutorials
  3. Enterprise AI Patterns (in Enterprise AI section)
  4. Production Deployment Tutorials
  5. Source Code Analysis (in Source Code section)
  • Getting Started – Environment setup and first applications.
  • Framework – Core abstractions: ChatClient, ChatModel, Prompt, Advisors.
  • RAG – Retrieval-Augmented Generation pipelines.
  • Vector Databases – Storage and retrieval of embeddings.
  • Providers – Integration guides for model providers.
  • Enterprise AI – Production patterns, security, observability.
  • Source Code Analysis – Internal architecture and extension points.