AI Hallucination Detection & Real Examples
AI hallucinations have not disappeared; they have become quieter. This guide shows how to detect unsupported numbers and assertions before they enter a report, spreadsheet, PDF, or other high-stakes deliverable.
Written by Rachel Hu
I’ve spent over a decade building secure AI systems for complex and high-stakes environments, from quant finance to scalable data science applications.
Trusted by 100k+ companies across the globe.
AI hallucination detection is the process of checking whether an AI-generated claim, number, or conclusion is supported by the original source material. It solves the problem of confident but incorrect output by recomputing results, tracing values to their source files and fields, and documenting what passed or failed. Analysts, finance and accounting teams, operations groups, engineers, researchers, and other teams use it when AI output must be reviewable and defensible.
An audit report turns an otherwise difficult review into an explicit result. The supplied example shows a report interface designed to make failures and supporting evidence visible rather than leaving the reviewer with an unexplained answer.
The demonstration describes an independent agent that double-checks every number, retraces figures to their source, verifies the result, and produces a report that can be reviewed. It is specifically positioned for reports that matter most.
A useful audit does more than say that an answer looks plausible. It identifies the source file, the extracted field, and the reference used for checking, creating a traceable chain for review.
AI hallucination detection does not have to be limited to one system’s own output. One shipped sample task is explicitly “Audit another AI’s work,” allowing the checking layer to remain separate from the agent that produced the deliverable.
What to do: Identify the numbers, assertions, calculations, and conclusions that could change a decision or make a deliverable difficult to defend. Include both individual values and the logic used to derive them.
What success looks like: The review has a clear set of testable claims rather than a vague request to “check the answer.”
Common mistake to avoid: Do not focus only on spelling or formatting while leaving the underlying figures unverified.
What to do: Gather the source documents used to create the output, including spreadsheets, PDFs, scans, CAD files, G-code, BOMs, DOCX files, or other supported formats.
What success looks like: Every claim can be compared with the material from which it was supposed to originate.
Common mistake to avoid: Do not validate against a copied summary when the original file is available.
What to do: Give the deliverable and source material to a second agent or separate checking process that did not produce the original answer.
What success looks like: The verification has a fresh perspective and no stake in defending the first result.
Common mistake to avoid: Asking the original agent whether it made a mistake can reproduce the same unsupported reasoning.
What to do: Recalculate totals, ratios, comparisons, and other derived values from the source data rather than accepting the generated result as correct.
What success looks like: The audit can show whether the reported value agrees with an independently recomputed value.
Common mistake to avoid: Comparing only the final total can miss an incorrect row or field that happens to cancel out elsewhere.
What to do: Record the exact source file, row, field, and reference behind every important number or assertion.
What success looks like: A reviewer can follow a clear chain from the final statement back to the underlying evidence.
Common mistake to avoid: A citation to a whole document is not enough when the relevant field or row can be identified.
What to do: Mark the deliverable as pass or fail, correct what can be corrected, and retain the evidence trail with the reviewed output.
What success looks like: You can explain what was checked, what failed, what changed, and why the final result can be trusted.
Common mistake to avoid: Do not silently edit a failed answer without preserving the original finding and its reason.
| Problem | Cause | Fix |
|---|---|---|
| A plausible number has no supporting source. | The model generated or inferred a value without grounding it. | Require a file, row, and field trace before accepting the number. |
| The total looks right but individual rows are wrong. | Errors can offset one another in an aggregate. | Recompute and inspect row-level values, not only the final total. |
| The original AI confirms its own answer. | The same reasoning path is being used for generation and review. | Use an independent auditor with no role in producing the first answer. |
| A reviewer cannot reproduce the conclusion. | The report lacks a clear evidence trail. | Attach the source reference and the checking result to each material claim. |
| Errors are found weeks or months later. | Validation happens after delivery or only during periodic review. | Audit each deliverable before it reaches the next reviewer or stakeholder. |
Energent Audit is designed as an independent AI auditor for checking deliverables produced by other AI agents. Its stated workflow recomputes numbers, traces them to source files and fields, fixes what it can, and produces a pass/fail result with evidence.
Use it when AI output needs a documented, source-grounded review; do not treat any automated audit as a substitute for judgment in situations that require human approval.
An AI hallucination is an unsupported or incorrect statement produced by an AI system. It can appear as a fabricated number, an inaccurate assertion, or a conclusion that is not grounded in the source material. Hallucinations can be difficult to notice because the wording may sound confident and professional. In a spreadsheet, report, PDF, or technical deliverable, the impact can be greater than a simple factual mistake. Detection therefore requires checking the output against the original evidence.
Begin by identifying the important figures in the AI-generated deliverable. Recompute those figures independently from the original source data, then trace each result to the exact file, row, and field used. Compare the recomputed value with the generated value and record any difference as a finding. Checking only whether the final answer looks reasonable is not sufficient because row-level errors can be hidden by an apparently correct total. A documented pass/fail verdict makes the result easier to review.
The original AI has already formed the reasoning that produced the deliverable. Asking it to validate its own answer can repeat the same unsupported assumption rather than challenge it. An independent auditor provides a second checking path with no stake in defending the original output. This separation is especially useful when the deliverable contains calculations or assertions that must be defensible. Energent Audit is described as a second agent that audits work produced by another AI, including a sample task named “Audit another AI’s work.”
A useful report contains a clear pass or fail verdict for the reviewed deliverable. It also identifies the claims or rows that failed and explains the evidence used to check them. For numbers, the report should show where the value came from and whether it was recomputed successfully. A source file, field, and reference create a traceable chain that another reviewer can follow. The report should preserve enough context to make the conclusion complete, cited, and reproducible.
Use it whenever an AI-generated result will influence a report, decision, stakeholder communication, or operational workflow. It is particularly relevant for finance and accounting, analytics, procurement, operations, engineering, research, and other work involving source documents or calculations. The most effective point is before delivery, because errors found after review can take much longer to correct. It can also help when reviewing output from an AI system that is not the platform performing the audit. Human judgment remains important for deciding how to act on a flagged result.
The reliable way to detect AI hallucinations is to separate generation from verification, recompute important numbers, trace every claim to its source, and preserve a clear pass/fail evidence trail. This turns review from an open-ended search for mistakes into a focused check of what was flagged. Energent Audit applies that model across documents, spreadsheets, scans, technical files, and other deliverables. If you want to test the workflow with your own material, you can begin with the product experience.