AI engineer shares a skill checklist spanning KV cache, quantization, and agent guardrails
ghumare64 · x · 2026-10-08
An AI engineer posted a checklist of skills beyond prompt engineering, split into two tracks:
Inference optimization
- KV cache management: eviction, reuse, and memory pressure at scale
- Prefill vs decode latency and why they optimize differently
- Continuous batching, PagedAttention, and throughput tuning
- Speculative decoding vs quantization vs distillation tradeoffs
- INT8/INT4/FP8/AWQ/GPTQ and when quantization hurts quality
- Prompt caching vs semantic caching
Engineering practice
- Harness and context engineering over long prompts
- Structured output failures: schema validation, repair loops, fallback chains
- Function calling reliability: tool contracts, argument validation, idempotency
- Agent guardrails: loop budgets, tool budgets, termination conditions
- Model routing, graceful fallback, and degraded-mode UX
- RAG architecture: chunking, embeddings, hybrid search, reranking, freshness
The core claim: AI engineering value lies in systems-level inference and reliability work, not prompts alone.
More from coding & agent
- Tavily, LangChain and Nebius host SF Tech Week event with 100K credits for startups — LangChain · 2026-10-08
- Dev ditches OpenClaw for Grok bot, citing speed and free live X access — haltakov · 2026-10-08
- Obsidian Starter Kit v5 automates archiving, todos and AI context so you stop doing them by hand — dSebastien · 2026-10-08
- Hamel Husain: same model as judge is usually fine — verify human-label alignment first — HamelHusain · 2026-10-08
- Mitchell Hashimoto's Rex: a terminal replacement built for AI coding agents — ricklamers · 2026-10-08
- DAIR.AI curates 31 papers on recursive self-improvement, from Gödel Machines to today — omarsar0 · 2026-10-08