Cadre

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.

Capstone 25 hr TypeScript (UI), Python / TypeScript (ingest + evals), SQL (ClickHouse)

Prerequisites: Phase 11 (LLM engineering), Phase 13 (tools), Phase 17 (infrastructure), Phase 18 (safety)

#Problem

Every AI team running production traffic in 2026 keeps an observability plane alongside the model. Cost attribution. Hallucination detection. Drift monitoring. Jailbreak signal. SLO dashboards. PII leak alerts. The open-source references — Langfuse, Phoenix, OpenLLMetry — converged on OpenTelemetry GenAI semantic conventions as the ingest schema. You can now instrument OpenAI, Anthropic, Google, LangChain, LlamaIndex, and vLLM with one SDK and ship compatible spans.

You will build a self-hosted dashboard that ingests from at least four SDK families, runs a small set of eval jobs over sampled traces, detects drift, and alerts. The measurement bar: given a deliberately injected regression (a prompt that starts producing PII), the dashboard catches it and fires an alert in under five minutes.

#Problem

Keep going. The rest of this lesson is one step away.