The AI Engineering Guide
aiengineering
Ten disciplines that turn a prompt into a reliable, production-grade LLM system — from serving architecture to the four-way tradeoff behind every design choice.
AI engineering is the full stack of decisions between a prompt and a system you can actually trust in production. This guide from Silverberry AI maps ten disciplines — serving, caching, compression, reliability, agents, retrieval, evaluation, observability, safety, and the core tradeoff — and the failure modes each one prevents.
Serving, caching, and compression
Inference has two phases with opposite bottlenecks. Prefill processes input tokens in parallel and is compute-bound. Decode emits one token at a time, reading the full KV cache each step, so VRAM bandwidth is the limit. Modern serving stacks handle this with:
- Continuous batching — interleave requests at different stages instead of waiting for a batch to drain.
- Paged attention — fixed-size KV pages that eliminate fragmentation and let requests share prefixes.
- KV cache hierarchy — hot sessions in GPU VRAM, warm in CPU RAM, cold on NVMe.
Caching splits into prompt caching (exact prefix, mathematically safe) and semantic caching (similar-query reuse, higher hit rate but needs a freshness check). Compression pairs quantization (FP8 near-lossless, INT8 usually safe, INT4 risky on reasoning) with speculative decoding for 2–3× faster decode at zero quality loss.
Reliability, agents, retrieval, and the four-way tradeoff
Models emit tokens, not API responses — so structured output needs a repair loop, and tool calls need versioned schemas and idempotency. Agents without nested budgets (cost ceiling, iteration cap, per-tool limits) become non-deterministic money burners. RAG is a seven-stage pipeline where quality can collapse at chunking, embeddings, recall, reranking, or freshness — which is why retrieval has its own metrics (recall@k, precision@k, MRR) separate from generation quality.
Evals catch regressions before users do. Observability attributes cost per token, per feature, per user journey. Safety is defense in depth — and every cache key must include the tenant ID or a semantic cache will leak one tenant's context to another.
Underneath all of it sits one mental model: latency, quality, cost, and reliability move together, and no choice is free. If you'd rather skip the plumbing and ship a specialist grounded in your own material, SNAP AI — no-code Agent Maker handles the engineering for you.