Spring AI with Java: Build AI-Powered Applications (2026)

Learn the core principles of prompt engineering and discover how better prompts produce more reliable AI responses.

Build advanced prompts using system, user, and assistant messages with Spring AI's fluent Prompt API.

Create reusable prompt templates with dynamic variables to reduce duplication and simplify prompt management.

Store prompts outside Java code and organize AI prompts for cleaner, production-ready applications.

Generate structured JSON responses and map AI output directly into Java DTOs using Spring AI.

Break complex AI tasks into multiple prompt interactions and build multi-step AI workflows.

Improve prompt quality, reduce ambiguity, and apply practical techniques used in production AI systems.

Learn why LLMs are stateless and why conversational AI applications require memory to maintain context.

Enable conversation memory using Spring AI ChatMemory and build multi-turn AI conversations.

Use Conversation IDs to isolate chat sessions and support multiple users and conversations.

Persist conversation history in a database and restore chat sessions after application restarts.

Explore sliding windows, summarization, and production memory strategies for long-running AI conversations.

Understand AI tool calling, function calling, and how Spring AI enables LLMs to execute Java methods.

Create your first AI tool, register it with Spring AI, and invoke Java methods from natural language.

Accept multiple tool parameters and integrate a real Weather API into your AI application.

Return structured Java objects from tools and let the AI generate natural language summaries.

Connect AI tools to application data using JDBC and automatically select the correct tool.

Chain multiple AI tools together and build multi-step workflows that combine tool outputs.

Learn practical techniques for designing secure, maintainable, and production-ready AI tools.

Learn why RAG exists and how it enables AI applications to answer questions using private documents.

Load PDFs, generate embeddings, and store searchable vectors in PostgreSQL using pgvector.

Retrieve relevant document chunks and answer questions using Spring AI and semantic search.

Extend your RAG application to search across multiple PDF documents using a shared vector store.

Build a custom RAG Advisor that combines retrieved document context with the LLM's general knowledge.

Explore vector storage, similarity search, retrieval flow, and practical techniques for production RAG systems.

Understand how modern AI models process both text and images using multimodal prompts.

Analyze images, identify objects, extract information, and ask questions about visual content.

Generate AI images from text prompts using OpenAI image models and Spring AI.

Build advanced image generation applications using Stability AI, styles, and aspect ratios.

Compare OpenAI and Stability AI for image quality, performance, flexibility, and pricing.

Explore speech recognition, speech synthesis, voice conversations, and enterprise Audio AI use cases.

Build the audio upload pipeline using Spring Boot and prepare files for AI processing.

Convert spoken audio into text using Spring AI transcription models and test multiple audio samples.

Combine speech recognition with ChatClient to build conversational voice-enabled AI applications.

Generate natural speech from AI responses and return audio directly from your Spring Boot application.

Integrate speech recognition, ChatClient, and speech synthesis into a complete voice AI assistant.

Connect a browser-based chat interface to your Spring AI backend and interact without Postman.

Learn why modern AI applications stream responses and how streaming improves user experience.

Implement streaming AI responses using Spring AI, Flux, and Server-Sent Events (SSE).

Display streaming AI responses in real time and build a ChatGPT-style browser experience.

Connect the browser UI with voice APIs to build an end-to-end conversational voice assistant.

Understand why MCP is needed beyond traditional tool calling and how MCP enables standardized communication between AI applications and independent services.

Explore MCP architecture, clients, servers, tools, resources, prompts, and enterprise use cases.

Build an independent Flight application and expose flight-search capabilities through an MCP Server.

Configure the MCP server, create MCP tools, and test the server using real MCP requests.

Connect the Travel AI Assistant to the Flight MCP Server and automatically discover its available tools.

Use natural language to invoke remote MCP tools and complete the end-to-end AI-to-MCP workflow.

Build a Hotel MCP Server and connect it alongside the Flight MCP Server to the Travel AI Assistant.

Learn automatic tool discovery and how an AI assistant can coordinate capabilities across multiple independent MCP servers.

Understand the limitations of demo AI applications and the key challenges enterprises face with scalability, security, governance, reliability, cost, and maintainability.

Build an AI Gateway to centralize LLM interactions, logging, timing, and common AI infrastructure. Refactor the Travel AI Assistant to use the gateway.

Build practical AI guardrails to enforce business policies before requests reach the LLM. Learn how policy-based validation improves reliability and control.

Implement PII protection using reusable redaction rules for email, phone, and passport information before sending user data to an LLM.

Implement intelligent response caching with Caffeine to reduce unnecessary LLM calls, improve response time, and lower AI application costs.

Bring the enterprise patterns together and understand how AI gateways, guardrails, MCP services, external systems, and LLM providers fit into a scalable architecture.