AI/ML Introduction for Java Developers
summary
Machine learning for Java developers emphasizes that models are non-deterministic and APIs are simple REST calls but the hard parts are architecture, data, and governance. Practical specifics: use Langchain4J or direct OpenAI/Anthropic REST calls, apply RAG with vector databases and embeddings (note: you cannot mix embedding types), and expect about 1.4 million models available. A chunking strategy often changes results more than swapping LLMs. A personal RAG built for guitar tutorials works in days; watch for hallucinations, negation failure, and compliance risks.
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chapters (17)
- 0:00Introduction to AI
- 2:13Determinism vs Non-Determinism
- 3:41Real-world AI Application
- 8:01AI/ML Evolution & Tools
- 11:18Patterns in Machine Learning
- 13:56AI Categories and Use Cases
- 16:56Applying AI in Software
- 18:51Understanding Language Models
- 23:27Prompts and Hallucinations
- 24:33Prompt Engineering Techniques
- 27:35Context in AI Models
- 31:24Retrieval Augmented Generation
- 39:43Productionizing AI Solutions
- 42:12Chatbot Architectures
- 43:20Java APIs and LangChain4j
- 45:45Collaborative AI Tools
- 48:42Final Thoughts and Advice
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