Software
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. The assistant feels
Software
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
AI
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–
AI
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 model, maybe a provider
AI
Introduction: Why We Needed a Question Classifier At Hoomanely, we receive thousands of pet‑related questions across health, behaviour, grooming, food, training and more. These queries help us understand what pet parents care about-and where our product needs to improve. But manually reviewing every question is slow, inconsistent, and impossible
AI
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 drowning in
AI
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: you