AI 202: Next-Level AI Mastery for Java Developers
summary
Walks through a runnable Spring Boot demo that wires Spring AI to OpenAI, uses the Tika document reader to ingest PDFs/docx, and stores embeddings in a Quadrant vector store for RAG. Shows concrete numbers: a sample prompt costs 133 tokens, a chat memory window trimmed to 10 items, and a retrieval that returns exact Piper Warrior V‑speeds (44 and 50 KIAS) from the ingested manual. Includes endpoints for chat, template prompts, translation, speech output files, image analysis (wake turbulence), and links to github.com/mkheck/ai202 and github.com/mkheck/rag.
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chapters (9)
- 0:00Introduction and context
- 3:51The new shiny paradox
- 5:55Applications for AI models
- 9:26Getting started with Spring AI
- 24:04Retrieval Augmented Generation
- 30:56Templates and prompting
- 34:03Conversation management
- 43:20Advanced media capabilities
- 52:02Resources and conclusion
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