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