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25+ curated questions with the same answer shape every time: quick recap, deep dive, pitfalls, and follow-ups. Coverage includes spring ai overview, prompt engineering java, llm api integration, rag patterns, and more.
Suggested path
Topics follow a teaching order. Each answer ends with a clear Next question control so you can move forward without hunting the list.
LLM-powered Spring applications are the newest backend interview surface. This module is an elevation arc: it opens with what Spring AI is and how it fits the Spring ecosystem, then prompt engineering (the lever you control), then wiring LLM APIs into a Spring app (rate limits, retries, function calling). From there it moves to RAG — the architecture most production LLM features use — and the vector databases that back it. It closes with the two tiers that separate shipped from demo: testing LLM features (evaluation, mocking) and observing them in production (cost, metrics, tracing).
Full catalog
Topics are ordered for learning. Within each topic, rows are numbered 1–25 globally so you always know where you are. Collapse topics if you want a lighter view — use Expand all when you need the full list again.
Numbers 1–25 follow the order we recommend — click any row to open the full answer, or use the sticky Next link at the bottom of each page to go Q1 → Q25.