Spring AI Roadmap
Build intelligent apps — chat, RAG, agents & production observability
Your Journey at a Glance
💡 How to use this roadmap
Work through each phase in order. Click on a skill to expand it — you'll find a description and curated resources. Don't rush; understanding beats speed. Complete one phase before moving to the next.
Foundations & Setup
Understand what Large Language Models are and how Spring AI abstracts them. Configure a provider (OpenAI, Ollama, Azure OpenAI, Anthropic) and make your first AI call with minimal boilerplate.
Chat, Prompts & Structured Output
Move beyond simple question-answer calls. Build multi-turn conversations, template complex prompts, parse model output into typed Java objects, and store conversation history.
Embeddings, Documents & Vector Stores
Give your AI access to your own data. Load documents, split them into chunks, convert to vector embeddings, store in a vector database, and retrieve semantically similar chunks at query time.
Agents & Tool Calling
Go beyond single-turn Q&A. Build agents that can call tools (functions, APIs, databases), reason across multiple steps, evaluate their own outputs, and collaborate via MCP.
Production, Observability & Staying Current
Ship AI features to production safely. Stream responses for interactivity, instrument AI calls with Micrometer, build Grafana dashboards, enforce cost guardrails, and build a system for staying ahead of Spring AI’s rapid release cadence.
Roadmap Complete!
You now have the foundations of a production-ready Java engineer. Apply by building real projects.
AI-Powered Customer Support Agent
A production-ready intelligent support agent backed by a private knowledge base. Users ask questions via a streaming chat UI; the agent retrieves answers from ingested PDF documentation, calls live tools to look up order status, and streams responses back in real time.
What you'll build
- ChatClient with system prompt persona and persistent JdbcChatMemory for multi-turn sessions
- PDF knowledge base: ingested with PagePdfDocumentReader, chunked via TokenTextSplitter, stored in PGVector
- RAG pipeline: QuestionAnswerAdvisor retrieves top-5 relevant chunks per query
- Tool calling: @Tool methods for order status lookup (REST), product search (DB), and ticket creation
- MCP server: exposes tools to Claude Desktop / other MCP clients for cross-model interoperability
- Streaming SSE endpoint: /api/chat/stream returns Flux<String> consumed with EventSource
- Micrometer AI observability: token usage counter, request latency histogram per model
- Grafana dashboard: tokens/s, p99 latency, error rate, cost estimate panel
- Agent evaluation suite: RelevancyEvaluator + FaithfulnessEvaluator run in CI via @SpringBootTest
Tech stack
Key highlights
- ✦Full RAG pipeline from PDF ingestion to grounded streamed answer
- ✦Zero hallucination guardrail: FaithfulnessEvaluator fails CI if score < 0.8
- ✦MCP server makes tools available to Claude Desktop without code changes
Key Resources for Spring AI
Handpicked books, websites, and YouTube channels to go alongside this roadmap.
- Building AI Applications with Spring AIOfficial Spring AI project page — start here for guides, docs, and release notes
- AI Engineering (Chip Huyen)Chip Huyen — production ML systems, RAG, agents, and LLM evaluation
- Prompt Engineering for LLMsJohn Berryman & Albert Ziegler — practical prompting patterns and techniques
- Spring AI ReferenceOfficial, always up-to-date Spring AI documentation
- Spring AI — GitHubSource, releases, changelog, and community discussions
- Spring BlogOfficial Spring team blog — Spring AI release posts and deep dives
- Model Context ProtocolOpen standard for connecting AI models to tools — increasingly relevant for agents