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cross_type 2.81 — strongly supporting 6 facts

Datadog's LLM Observability platform is specifically designed to detect, visualize, and analyze the root causes of {concept:hallucination} in AI applications, as detailed in [1], [2], [3], [4], [5], and [6].

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Detect hallucinations in your RAG LLM applications with Datadog ... datadoghq.com Barry Eom, Aritra Biswas · Datadog 6 facts

procedureDatadog's LLM Observability allows users to drill down into full traces to identify the root cause of detected hallucinations, displaying steps such as retrieval, LLM generation, and post-processing.

procedureThe Traces view in Datadog's LLM Observability allows users to filter and break down hallucination data by attributes such as model, tool call, span name, and application environment to identify workflow contributors to ungrounded responses.

claimDatadog's LLM Observability provides an Applications page that displays a high-level summary of total detected hallucinations and trends over time to help teams track performance.

claimWhen Datadog's LLM Observability detects a hallucination, it provides the specific hallucinated claim as a direct quote, sections from the provided context that disagree with the claim, and associated metadata including timestamp, application instance, and end-user information.

claimUsers can visualize hallucination results over time in Datadog's LLM Observability to correlate occurrences with deployments, traffic changes, and retrieval failures.

claimDatadog's LLM Observability platform provides a full-stack understanding of when, where, and why hallucinations occur in AI applications, including those caused by specific tool calls, retrieval gaps, or fragile prompt formats.