Lesson 981 of 2116
LLM Observability Tools: What to Trace, What to Sample, What to Alert
LLM observability tools (LangSmith, LangFuse, Helicone, Datadog LLM, custom) all trace conversations. The differentiation is in evaluation, dashboards, and alerting — and choosing the wrong tool wastes months.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2AI Observability Platforms: Choosing Among LangSmith, Arize, and Helicone
- 3The premise
- 4AI tools: observability for LLM apps
Concept cluster
Terms to connect while reading
Section 1
The premise
LLM observability tool selection depends on your specific needs; the wrong choice produces months of pain.
What AI does well here
- Identify your highest-priority observability needs (production debugging, evaluation, cost tracking, drift detection)
- Evaluate tools against those needs, not generic feature lists
- Build the tracing schema before picking a tool (data model first)
- Plan the integration cost (instrumentation, retention, retrieval)
What AI cannot do
- Get observability without instrumenting your code
- Substitute tool selection for thinking about what you need to observe
- Avoid some operational burden (every tool requires maintenance)
Key terms in this lesson
Section 2
AI Observability Platforms: Choosing Among LangSmith, Arize, and Helicone
Section 3
The premise
AI can compare observability platforms across criteria and your stack, but procurement and security review own the final selection.
What AI does well here
- Draft platform comparison matrices on tracing, eval, and pricing.
- Generate proof-of-concept evaluation plans for shortlisted vendors.
What AI cannot do
- Negotiate enterprise pricing with vendors.
- Replace security and compliance review.
Section 4
AI tools: observability for LLM apps
Section 5
The premise
LLM apps fail in ways traditional APM misses: silent quality regressions, prompt drift, cost spikes from one runaway user. Per-call logging of prompt, completion, latency, cost, and a quality signal makes incidents diagnosable.
What AI does well here
- Emit structured logs when wired to a tracing library
- Surface latency and token counts per call
- Correlate calls within a single trace when given trace IDs
What AI cannot do
- Tell you whether the output was good without an external judge
- Self-report cost across nested tool calls accurately
- Detect quality drift on its own
Section 6
Langfuse: Observability for AI Application Stacks
Section 7
The premise
Langfuse traces LLM and agent applications: prompts, completions, tool calls, costs, latencies. It's how you debug 'why did the agent do that' once you have real users.
What AI does well here
- Trace nested LLM and tool calls through complex agent flows
- Track cost and latency per user, session, and route
- Run prompt-level evaluations against production traffic
What AI cannot do
- Replace product analytics for non-LLM user behavior
- Substitute for a proper evaluation harness on critical flows
- Catch silent quality regressions without explicit eval setup
End-of-lesson quiz
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