This two-part series explores LLM observability and traceability, focusing on the LangSmith platform. Part 1 details how to make LLM applications replayable and create tamper-evident audit logs using LangSmith's tracing capabilities and custom callbacks. Part 2 addresses how to prevent regressions by implementing datasets, evaluators, and experiments, akin to traditional software regression testing, and discusses choosing the right tooling stack. AI
IMPACT Provides developers with tools for robust LLM application management, including regression testing and audit trails.
RANK_REASON The cluster discusses a specific software product, LangSmith, and its features for LLM observability and testing, rather than a new model release or research paper.
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