Systems engineering for production LLMs: model routing by cost and capability, backpressure under load spikes, session continuity across a stateless backend, and validating structured output before it reaches a user.
If you've ever shipped a RAG system into production, you already know the uncomfortable truth: these systems don't fail dramatically. They fail quietly. No alarms. No error stacks. No catastrophic crashes. One day the answers simply aren't as sharp as they used to be.
AI assistants are becoming deeply embedded across apps, devices, and internal tools. They read context, recall previous turns, guide users through troubleshooting, analyze data, and invoke tools on behalf of the user. Yet the systems beneath them, models, prompts, routing layers, retrieval indexes, formatting logic, safety filters, are in constant
Introduction Choosing the right language model architecture for production is one of the most underrated engineering decisions teams make. Behind every AI feature - from a chatbot response to a search ranking - sits a strategic decision: Should we use an encoder-only model? A decoder-only model? Or a full encoder–
The first LLM integration is usually simple: you pick one provider, one model, one API key, and ship. The second and third are where things start to hurt. Suddenly you have a "cheap" model for bulk tasks, a "smart" model for critical flows, maybe a vision
Why we needed a question classifier At Hoomanely, pet parents send us thousands of questions covering health, behavior, grooming, food, and training. Those questions tell us what people actually care about and where the product falls short, but reading every one by hand is slow, inconsistent, and doesn't
LLM features usually start life in a happy place: a handful of users, low traffic, and plenty of quota. Latency looks fine, tokens are cheap, logs are quiet. Then a product launch, a marketing campaign, or a new "Ask AI" button lands, and suddenly your once-stable service is
Modern AI products feel personal only when they remember, that bug you were debugging yesterday, the pet profile you set up last month, the long-running coaching thread you keep coming back to. Under the hood, though, LLM inference APIs don't remember anything. Every call is a clean slate,
Putting an LLM in a production path means treating it like any other unreliable, expensive, rate-limited dependency — not a magic black box. This hub covers the systems engineering around large language models: routing requests to the right model, handling load spikes without falling over, keeping conversations coherent across a stateless service, and validating that structured output is actually correct before it reaches a user.
We work on this because our AI health assistant has to stay useful and affordable at scale — a naive integration gets expensive and brittle fast, and a hallucinated structured response in a pet health context is a real trust problem, not a curiosity. So we treat reliability and cost control here with the same rigor we apply to firmware.
Policy-Driven Model Routing: Selecting the Right LLM Per Request covers how we pick a model per request based on cost and capability. Backpressure for LLMs: Managing Load Spikes and Token Floods in Real-Time AI Services is our mental model for handling traffic surges gracefully. Building Stateful Continuity in Stateless LLM Services: A Multi-Tier Session Architecture covers how conversations stay coherent across a stateless backend. Beyond Structured Outputs: The Validation Layer LLMs Still Need covers what has to happen after the JSON schema passes. Retrieval Cost Engineering for OpenSearch RAG and Bounded Intelligence: Operating AI Systems That Remain Stable, Predictable, and Trustworthy round out the cost and governance side of running LLMs in production.