# AI observability

Logging every AI interaction — inputs, outputs, tokens, latency, cost, user feedback — with tools like LangSmith, Langfuse, or Helicone. You can’t improve, debug, or budget what you can’t see. Instrument from day one.

Canonical: https://robauto.ai/learn/ai-architecture/11

_Advanced AI Architecture: Strategy, Stack & Daily Practice — lesson 11 of 20 (DEFINITION)_

Logging every AI interaction — inputs, outputs, tokens, latency, cost, user feedback — with tools like LangSmith, Langfuse, or Helicone. You can’t improve, debug, or budget what you can’t see. Instrument from day one.

Source: [Langfuse — LLM observability](https://langfuse.com?utm_source=robauto)

[Previous lesson](/learn/ai-architecture/10) · [Next lesson](/learn/ai-architecture/12) · [Course overview](/learn/ai-architecture) · [All courses](/learn)

---

(c) 2026 Robauto, Inc. — support@robauto.ai
Machine surfaces: https://robauto.ai/llms.txt · https://robauto.ai/llms-full.txt · https://robauto.ai/.well-known/api-catalog