#llmops
4 articles
Design an LLM Observability Platform
Build the distributed tracing backbone for non-deterministic, multi-step LLM applications — capturing every prompt, completion, token count, and dollar cost across chains, retrievals, and tool calls so you can debug a failed agent run and account for every cent.
Design an LLM Gateway (AI Gateway & Model Router)
A single proxy control plane in front of OpenAI, Anthropic, Google, and open models — routing ~65 trillion tokens a month with automatic failover, semantic caching, per-team budget enforcement, and streaming SSE passthrough, all under 50 ms of added latency.
Design an LLM Evaluation Platform
Build the system that tells a team whether a prompt or model change made the product better or worse — automatically. Covers offline eval with LLM-as-judge, CI regression gating, online production sampling, human annotation queues, and eval for RAG, agents, and classifiers at the scale of 450 million evaluations per month.
Design an AI Guardrails & Safety System
Build the validation layer that wraps every LLM call — detecting prompt injections, redacting PII, catching toxic outputs, and verifying groundedness — while staying inside a 200 ms latency budget for 10 million daily requests.