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# You launched an AI assistant. Do you really know how it's performing?

**[Whitespectre](https://daily.dev/sources/whitespectre)** · 11 min read · 1 upvotes · 0 comments

## Summary

AI assistants require evaluation at three levels: individual turns, full sessions, and cohort trends. A framework combines evaluation (defining quality metrics), observability (capturing system behavior), and traceability (connecting failures to causes). Quality assessment uses CORE dimensions (clarity, relevance, tone, accuracy) plus CUSTOM product-specific metrics, weighted by importance. Hybrid review pipelines combine manual calibration with automated LLM-as-judge scaling. Success requires instrumenting telemetry across model, retrieval, and tooling layers, then connecting conversation quality to business outcomes like retention and deflection.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.whitespectre.com/ideas/Evaluating-Conversational-AI-in-Production>

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---

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

[View this post on daily.dev](https://daily.dev/posts/you-launched-an-ai-assistant-do-you-really-know-how-it-s-performing--wsd6k7387)

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