As an AI Engineer. Please learn:
Harness engineering, not just prompt engineering
Prompt caching vs. semantic caching tradeoffs
KV cache management at scale
Speculative decoding vs quantization
Structured output failures & fallback chains
Evals (LLM-as-judge + human evals)
Cost attribution per feature, not just per model
Agent guardrails & loop budgets
LLM observability as a first-class discipline
Model routing & graceful fallback logic
Knowing when to fine-tune vs. in-context learning