Capstone 11 — LLM Observability & Eval Dashboard
Langfuse went open-core. Arize Phoenix published the 2026 GenAI semconv mappings. Helicone and Braintrust both doubled down on per-user cost attribution. Traceloop's OpenLLMetry became the de-facto SDK instrumentation. The production shape is ClickHouse for traces, Postgres for metadata, Next.js for UI, and a small army of eval jobs (DeepEval, RAGAS, LLM-judge) running over sampled traces. Build one self-hosted, ingest from at least four SDK families, and demonstrate catching an injected regression in under five minutes.
This lesson isn’t interactive yet.
It is part of the curriculum and will get the same treatment as Phase 1 — a visual cover, hands-on labs, derivations with numeric checks and a quiz. Until then, the original lesson is the best place to read it:
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