Why this matters
- Spring AI standardises chat, embedding, and vector-store clients behind Boot configuration.
- BookStore recommendation features need guardrails — prompts, retrieval, and observability.
- Staff interviews ask how you avoid leaking PII into model providers.
Spring AI role
L1 · FresherUnified clients for chat and embedding models so BookStore can add recommendations or support bots.
Prompt templates
L2 · IntermediateExternalize prompts in configuration; version them like code and log redacted inputs in prod.
RAG overview
L3 · AdvancedRetrieve catalog snippets from a vector store before calling the LLM to ground answers in real inventory.
Safety and cost
L4 · ExpertRate-limit AI endpoints, filter PII, and cache embeddings — monitor token spend per tenant.
Hook
BookStore support wants semantic search: “cozy mystery set in Oslo” instead of keyword title LIKE. Spring AI wires chat and embedding models into Boot 3.5 beans with the same configuration style as DataSource setup.
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Still to come · 5 sections
- Boot starter and configuration
- ChatClient for descriptions
- Runnable BookStore lab
- Failure modes in production
- Testing without live API
Test yourself
Answer these before moving on — recall is what makes it stick.