From Chat to RAG to MCP: Enhancing Java Applications with AI
summary
Shows a Java prototype that turns freeform text, photos and voice into structured Java objects via Spring AI and LangChain4J, uses embeddings (vectors ~ hundreds of dims) and a vector DB for RAG to retrieve candidate product rows, then resolves IDs against the catalog before returning results, and can delegate orchestration to the model via MCP (model requests function calls, app executes). Notes working limits: context windows up to ~50,000 tokens, “lost in the middle” on ~1,000-item lists, judge model can be a smaller, cheaper verifier; latency, token cost and observability require engineering tradeoffs.
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chapters (12)
- 0:00Introduction to AI in Java
- 3:04How LLMs work under the hood
- 10:33Why Java developers should care
- 12:23Practical Java LLM integration
- 18:12Augmenting existing Java apps
- 23:13Retrieval Augmented Generation explained
- 29:40Vector databases and embeddings
- 32:13Model Context Protocol basics
- 35:23Performance and cost considerations
- 37:11RAG vs MCP comparison
- 38:14Measuring AI business value
- 39:40Safeguards and real-world risks
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