AI Engineering

Why can one request with a long input disrupt users whose answers are already streaming?

An LLM answer needs repeated computation and growing memory. Follow what happens when many users need both at once.

A one-year special edition on distributed systems, databases, and production AI.

Six prompt patterns with evidence behind them, the mechanisms that make them work, and the checks that keep them working as models change.

Three tools, one decision. Here's how to choose, and why it's usually not the one you think.

Your LLM will try to do things it shouldn't. Here's how to stop it.

LLMs break traditional observability. Log tokens, trace multi-step chains, attribute costs, monitor quality with evals, and handle privacy deliberately.

Three seconds will still feel like forever to your users. Make sure they never have to wait that long staring at a spinner.

Your LLM costs more than you think - here's the full picture

Unserstanding deeply what actually breaks in production

128K tokens doesn’t mean you should use 128K tokens