hallucination-investigator

To find the root cause of AI hallucinations, you reconstruct failing outputs with available evidence to identify missing context, retrieval gaps, prompt framing issues, or tool misuse. This process pinpoints whether failures stem from data limitations or unsupported inferences.

Factuality failure analysis diagnoses why AI outputs are inaccurate by comprehensively examining context, retrieval quality, prompts, and tools. It reconstructs the failure with evidence to determine root causes and proposes high-leverage fixes to prevent recurrence.

Debug retrieval gaps by analyzing failing AI responses alongside relevant context to identify missing information. Root-cause analysis determines if inaccurate outputs stem from retrieval quality, prompt framing, or unsupported inferences, enabling targeted fixes.

Fix factuality issues by proposing actionable improvements based on identified root causes across context, prompts, and tools. Prevent recurrence by applying guardrails and running verification scenarios that validate the fixes against future failures.

Unsupported inferences occur when AI outputs lack sufficient context or retrieval evidence. Root-cause analysis reconstructs the failing example to determine if the failure stems from data limitations, prompt framing, or tool misuse causing the unsupported inference.

Yes, root-cause analysis diagnoses tool misuse by examining failing examples across your workflow design. It identifies whether inaccurate outputs originate from tool limitations, retrieval gaps, or prompt framing, and proposes high-leverage fixes with verification scenarios.