An AI engineer’s learning list covers caching, routing, RAG, and quantization
EmployerNegative5653 · reddit · 2026-07-23
An AI engineer posted a practical checklist of the infrastructure topics people should actually learn, instead of stopping at prompt engineering.
The list covers
- harness engineering and context engineering
- prompt caching vs. semantic caching
- KV cache management, eviction, reuse, and memory pressure
- prefill vs. decode latency
- continuous batching and paged attention
- speculative decoding, quantization, and distillation
- INT8 / INT4 / FP8 / AWQ / GPTQ tradeoffs
- structured output failures and repair loops
- function calling reliability and idempotency
- agent guardrails, tool budgets, and termination conditions
- model routing and degraded-mode UX
- RAG design: chunking, embeddings, hybrid search, reranking, freshness
It is basically a compact syllabus for building real LLM systems at scale.
More from Infra
- Celeris launches a lab to build an LLM with microsecond response times — timshi_ai · 2026-07-23
- Reddit thread asks how to catch runaway agents before they blow the budget — Designer_Power3691 · 2026-07-23
- A curated guide to LLM cache management spans KV cache, batching, and decoding — gaganghotra_ · 2026-07-23
- RunPod users get a Chrome extension that notifies and auto-claims saved pods — Particular-Repair895 · 2026-07-23
- MiniMax says MI355X is nearing B200 for serving its 428B multimodal model — salykova_ · 2026-07-23
- Aurora launches an open-source Go gateway for routing and securing LLM traffic — Select-Medicine-9310 · 2026-07-23