As an AI Engineer. Please learn: Harness engineering, not just prompt engineering Context engineering, not just long prompts Prompt caching vs. semantic caching tradeoffs KV cache management, eviction, reuse, and memory pressure at scale Prefill vs. decode latency and why they optimize differently Continuous batching, paged attention, and throughput optimization Speculative decoding vs. quantization vs. distillation tradeoffs INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality Structured output failures, schema validation, repair loops, and fallback chains Function calling reliability, tool contracts, argument validation, and idempotency Agent guardrails, loop budgets, tool budgets, and termination conditions Model routing, graceful fallback logic, and degraded-mode UX RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness Retrieval evals: recall, precision, grounding, attribution, and citation quality Evals: golden sets, regression tests, adversarial tests, LLM-as-judge, and human evals LLM observability as a first-class discipline: traces, spans, tokens, latency, errors, and drift Cost attribution per feature, workflow, tenant, and user journey not just per model Safety engineering: prompt injection defense, data leakage prevention, and permission boundaries Multi-tenant isolation, cache safety, and cross-user context contamination prevention Fine-tuning vs. in-context learning vs. RAG vs. distillation and when each is the wrong tool Latency, quality, cost, and reliability tradeoffs across the full inference stack Production failure modes: hallucinated tool calls, malformed JSON, stale retrieval, runaway agents, and silent eval regressions Shipping LLM systems as reliable infrastructure, not demos wrapped around prompts aiengineeringfromscratch.com