Topic
9 guides on middleware — architecture patterns, rollout strategy, and production integration for engineering teams.
MCP standardizes how agents connect to tools and data — but it does not replace LLM middleware, tenant auth, or observability. A decision guide for product and platform teams.
Read moreLangfuse 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 embedded copilots: context from product state, server-side assembly, RBAC, and UI patterns that fit existing workflows instead of a floating chat widget.
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 moreA practical security guide for multi-tenant products — why system prompts are not enough, where attacks actually land, and the integration patterns that hold up in production.
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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