Managing LLM context in a real application

Ahnii! This post covers how Claudriel, a Waaseyaa-based AI assistant SaaS, handles LLM context in production: conversation trimming, per-task turn budgets, model degradation on rate limits, prompt caching, and per-turn token telemetry. The problem with unbounded context Every message you send to an LLM API costs tokens. Long-running chat sessions accumulate history fast. Left unchecked, a single active session can push input token counts into the tens of thousands per turn, even bef