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