model evaluation and observabilityLangSmith: uses, limits and practical trial
LangSmith collects and inspects AI application traces with quality monitoring, evaluation datasets and user feedback.
Sources consulted on · LangChain
Directory facts
- Publisher / organisation
- LangChain
- Primary use
- model evaluation and observability
- Related directory
- Explore this family’s services
Suitable tasks
Connect an incorrect answer to the retrieval, model call or tool steps that produced it.
Limits and checks
A trace can contain sensitive inputs, documents and outputs. Define what is recorded before instrumentation.
A repeatable trial
Using fictional data, trigger a missing reference and a tool error. Check their distinction in the trace and masking of a test confidential marker.
How to decide
Keep instrumentation when it explains failures without retaining more data than needed.
Frequently asked questions
Does a successful trace prove a correct answer?
No. Technical success and output quality need separate criteria.
Official documentation and scope
Functions are described from documentation. Proposed trials are editorial advice, not executed benchmarks. Check prices, quotas, access and conditions before choosing.
Consultation covers identification and described functions; performance and all contractual conditions were not tested.
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