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# LLM Observability, Evaluation, Experimentation Platform — Dat Ngo, Arize

**[AI Engineer](https://daily.dev/sources/aidotengineer)** · 16 min read · 1 upvotes · 0 comments

## Summary

A talk by an AI architect at Arize AI covering three pillars of making LLM-based systems work in production: observability, evaluation, and experimentation. Observability is built on OpenTelemetry traces and spans, with views into sessions, runs, and distributional agent behavior. Evaluation is broken into five signal types (LLM-as-judge, human feedback, golden datasets, deterministic checks, business metrics) and can be scoped at span, multi-span, trajectory, or session level. Experimentation involves collecting datasets from traces and running prompt/model/config changes. Arize offers two products: open-source Arize Phoenix (single container, no Kubernetes) and enterprise Arize AX. The company's stated goal is to automate the entire observability-eval-experimentation flywheel via an AI assistant called Alex.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=JsCCrBF7F1g>

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#llm](https://daily.dev/tags/llm), [#llm-observability](https://daily.dev/tags/llm-observability), [#opentelemetry](https://daily.dev/tags/opentelemetry)

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