Topic
6 guides on observability — architecture patterns, rollout strategy, and production integration for engineering teams.
Langfuse covers traces and evals. Here is what else production teams need: structured logging, OpenTelemetry metrics, quality signals, sampling, canaries, and when to add Braintrust, Phoenix, or your existing APM.
Read moreA practical guide to LLM cost observability: structured logging, Langfuse dashboards, OpenTelemetry metrics, per-tenant budgets, and the unit economics finance actually needs.
Read moreA practical guide to golden sets, property-based scoring, and CI gates — so prompt and retrieval changes do not silently break production copilots.
Read moreHow to trace model calls, debug prompts, and run evals with Langfuse — integrated into server-side LLM middleware, not bolted onto a frontend demo.
Read moreA practical guide to the server-side layer between your app and the model — auth, rate limits, routing, logging, and the patterns that keep AI features production-ready.
Read moreA practical checklist for engineering leaders — beyond the demo and before you call an AI feature shipped.
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