AI Engineering · Phase 4
The model-side knowledge that separates an integrator from an AI engineer: transformers, fine-tuning, vector search internals, evaluation and LLMOps.
Understand the foundation models you build on: GPT, Claude, Gemini, Llama and Mistral families, how they differ, and a practical framework for choosing a model for your Java application.
Read tutorialThe transformer architecture explained for engineers, not researchers: self-attention, multi-head attention, positional encoding and why it explains context limits, token cost and hallucination.
Read tutorialUnderstand fine-tuning for engineers: LoRA and QLoRA, instruction tuning, RLHF and DPO, and the crucial decision of when to fine-tune versus when retrieval or prompting is the better tool.
Read tutorialHow vector databases work under the hood: the HNSW index, approximate nearest-neighbour search, cosine vs Euclidean distance, product quantization and metadata filtering — for Java developers.
Read tutorialHow embedding models power semantic search: bi-encoders vs cross-encoders, re-ranking, hybrid search combining keywords and vectors, and choosing embeddings for retrieval quality.
Read tutorialAdvanced prompt engineering techniques: prompt chaining, meta-prompting, self-consistency, structured reasoning and prompt optimization — beyond the basics, for reliable production prompts.
Read tutorialUnderstand tokens and context windows: how BPE tokenization works, why code costs more tokens, managing the context budget, and the token math behind LLM cost — for Java developers.
Read tutorialHow to evaluate LLMs and LLM applications: what public benchmarks like MMLU and HumanEval measure, their limits, and building a custom evaluation suite that reflects your real task.
Read tutorialMake models smaller and faster: quantization (GGUF, GPTQ, AWQ), knowledge distillation, the accuracy-vs-efficiency trade-off, and when self-hosting a compressed model makes sense.
Read tutorialBuild guardrails around LLMs: input filtering, output validation against schemas and rules, content moderation, jailbreak defense and layered safety — deterministic controls in Java.
Read tutorialThe operational practice of running LLM features: prompt versioning, evaluation in CI/CD, model registries, A/B testing and canary rollouts of prompt and model changes — for Java teams.
Read tutorialRun generative AI on the major clouds from Java: Amazon Bedrock, Azure OpenAI and Google Vertex AI compared, with Spring AI and LangChain4j integration and how to choose.
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