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