Evaluation tools: LangSmith, Langfuse, and Arize Phoenix
Words made simple
A few useful meanings before you read the explanation.
- Evaluation
- Checking results against examples and clear criteria to find whether the system works well and where it fails.
- AI
- Software techniques for tasks such as recognizing patterns, understanding language or generating content.
- Retrieval
- Finding and fetching information that is useful for the current question.
More words from this lesson
- Tenant
- A customer or organization whose data must stay separate from other customers in a shared application.
- Observability
- Being able to understand system behavior through records such as logs, measurements and traces.
- Trace
- A linked record of the steps in one request, including model calls, retrieval and tools. It helps locate delays or failures.
- Latency
- How long a request takes to return a result. Lower latency usually means a shorter wait for the user.
- HTTP
- The request-and-response protocol used on the web. HTTPS adds encryption to protect information in transit.
Overview
- These platforms help collect traces, datasets, scores, and experiment results.
- LangSmith integrates closely with the LangChain ecosystem; Langfuse provides AI observability and evaluation workflows; Phoenix supports tracing and evaluation analysis.
- They are tools for implementing evaluation, not definitions of what quality means.
Why this matters
Engineers need reproducible comparisons and a way to inspect failures across runs.
An everyday example
A test laboratory records results, but you still define what passes inspection.
A technical example
Compare prompt versions on a claims dataset and drill into cases where policy evidence was missing.
How it works
- Instrument calls
- Attach version tags
- Upload permitted cases
- Run scoring
- Compare experiments
- Inspect traces and add regression cases.
Architecture and flow
Application/eval runner → traces + dataset + scores → evaluation platform → decision.
The insurance assistant example
The insurer tracks unsupported-policy-answer rate by product and policy version.
When to use it
When trace inspection and experiment management exceed a simple local report.
When to avoid it
Do not upload raw private cases without approved handling or choose a platform before defining the quality rubric.
Interview answer
“Evaluation platforms organize evidence and comparisons. I still own datasets, risk-based metrics, release thresholds, and privacy controls.”
Benefits, trade-offs, and production considerations
- Check hosting, retention, export, sampling, and cost.
- LangSmith, Langfuse, Arize Phoenix.
Example release gate — illustrative targets, not industry guarantees: zero unauthorized writes or tenant leaks in the security suite; all business-rule fixtures pass; retrieval recall and final correctness meet task-specific targets; p95 latency and cost per completed task stay within the agreed budget. Report counts and uncertainty and investigate every severe failure even when averages pass.
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Evaluation tools: LangSmith, Langfuse, and Arize Phoenix
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These platforms help collect traces, datasets, scores, and experiment results. LangSmith integrates closely with the LangChain ecosystem; Langfuse provides AI observability and evaluation workflows; Phoenix supports tracing and evaluation analysis. They are…
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Next lesson: Traces, agent runs, LLM/tool calls, and state transitionsAdapted from 13_AI.MD · “Evaluation tools: LangSmith, Langfuse, and Arize Phoenix”. Examples are study fragments; framework behavior can depend on your version.