AI quality assurance guide
AI-generated reports, spreadsheets, dashboards, and recommendations can look polished while containing wrong denominators, unsupported claims, missing rows, or subtle formatting failures. This guide explains how to audit the work independently: identify sources, recompute material numbers, trace claims, test data quality, validate the final artifact, and issue a clear verdict. It is written for analysts, finance and accounting teams, operations leaders, researchers, engineers, and anyone responsible for high-stakes AI-assisted work. The fastest reliable approach is to let an independent review verify every material claim and focus human attention only on what is flagged.
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.
An AI audit is an independent quality check performed by a second agent or reviewer that is separate from the AI that produced the original work. It verifies calculations, traces claims to source files, tests whether conclusions are supported, checks the integrity of the output artifact, and assigns a pass, partial, fail, or warn verdict. The purpose is to replace “I have to verify everything” with “I only need to look at what is flagged.”
List the source files, deliverables, claims, calculations, charts, recommendations, dates, and geographic or time boundaries. A trace should identify files such as bls_private_average_hourly_earnings_2017_2026.json and the deliverable that used them.
Do not accept a plausible number without recalculating it. For example, $1,284,500 across 412 rows reconciled correctly, while a growth calculation exposed 12.0% as the correct increase instead of 18%.
Each material claim should show its source file, row, field, cell, transformation, and final location in the deliverable. A reviewer should be able to answer where the number came from, how it changed, and whether the result can be reproduced.
For deeper guidance, use source-grounded verification as the standard for important outputs.
Mark facts, derived figures, reasonable interpretations, unsupported claims, and claims requiring missing data separately. The correct workflow is to label an explanation as an inference rather than presenting it as proven causation.
A dedicated hallucination detection review helps expose unsupported conclusions before delivery.
Check missing values, duplicates, invalid records, joins, identifiers, and internal consistency before interpreting the result. In one tracking dataset, 11,801 sessions lacked source and campaign tags, while 6,075 had HTTP referrers and 5,726 lacked both source and referrer.
Inspect spreadsheets, PDFs, dashboards, charts, screenshots, links, and interactive elements. An output can contain correct data yet still fail because a chart omits the final row or a right-to-left workbook places its primary index on the wrong side.
An audit report should make findings and supporting evidence reviewable.
The demonstration describes tracing figures back to their sources before delivery.
Record the source files, deliverables, claims, calculations, charts, recommendations, date, and scope. For example, connect create_mavenfuzzyfactory_vApril2022.sql and preparing_workbench_vApril2022.sql to the reports that used them.
Success looks like: Every output has a named, accessible source and a defined coverage period.
Common mistake to avoid: Do not audit a conclusion without recording which source data and transformations produced it.
Recalculate totals, percentages, growth rates, ratios, denominators, and derived values independently. A total of $1,284,500 across 412 rows reconciled to the deliverable, but the correct Q4-to-Q1 increase was 12.0%, not 18%, because the original calculation used a subtotal that excluded Facilities.
Success looks like: Your independent result matches the deliverable or produces a documented discrepancy.
Common mistake to avoid: Never accept a plausible result without testing the denominator and period used.
For every important claim, document the source file, row, field, cell, transformation, and location in the final output. The trace should allow another reviewer to reproduce the result without relying on the original AI agent.
Success looks like: A reviewer can answer where the claim came from, how it was transformed, and where it appears.
Common mistake to avoid: A general source citation is not enough when a precise row, field, or cell is available.
Classify statements as directly observed, correctly derived, reasonable but unproven, unsupported, or dependent on missing data. For example, software spending of $298,000 may be supported, while a claim that it grew 30% quarter over quarter fails if no prior-quarter figure exists.
Success looks like: Facts, calculations, caveats, and hypotheses are visibly distinguished.
Common mistake to avoid: Do not present correlation or a likely explanation as proven causation.
Check missing values, duplicates, invalid values, IDs, joins, negative prices, missing session IDs, missing product IDs, and agreement between line-item sums and order totals. Separate legitimate unattributed records from broken or incomplete records.
Success looks like: You can explain which records are missing or unusual and whether they affect the conclusion.
Common mistake to avoid: Do not interpret a trend until you know whether the underlying dataset is complete enough for that interpretation.
Inspect the actual artifact, not just the text describing it. In the RTL dashboard example, the setting was enabled, but the primary index appeared in Column B and charts stopped at row 6 even though the table continued to Africa at row 7.
Success looks like: The output satisfies its requested format, layout, chart range, labels, links, and interactive requirements.
Common mistake to avoid: Do not treat a polished appearance as evidence that the artifact is compliant.
Use Pass for correct and supported work, Partial for a correct result with a methodological caveat, Fail for incorrect, unsupported, or noncompliant work, and Warn for a potential issue requiring reviewer attention.
Success looks like: Every material check has one verdict and supporting evidence.
Common mistake to avoid: Do not hide a failed requirement inside an otherwise positive overall summary.
State what passed, what failed, what is partial, what must be corrected, which evidence supports each conclusion, and what limitations remain. A strong verdict might say that source-backed totals reconcile, but the growth rate must be restated as 12.0% and the unsupported software trend claim removed or qualified.
Success looks like: The reader knows exactly whether the work can be published and what remains unresolved.
Common mistake to avoid: Do not issue a vague “mostly accurate” conclusion when specific corrections are available.
| Problem | Cause | Fix |
|---|---|---|
| Wrong growth percentage | The model used a subtotal or incorrect denominator. | Recompute from complete period totals and disclose the denominator. |
| Unsupported trend claim | The required comparison period is absent. | Remove the claim or label it as unverifiable because the input is missing. |
| Correct total with accounting caveat | Annual prepayments were included without discussing treatment. | Keep the reconciled total, then disclose that $84,000 may require amortization. |
| Chart omits final row | An off-by-one range stops before the final record. | Compare the chart range with the table’s final row and extend the reference. |
| National evidence presented as local proof | The model exceeded the geographic scope of the data. | Label the figures as national proxies and do not claim local demand or adoption without local evidence. |
Energent.ai positions its independent AI auditor as a way to verify outputs produced by other AI agents against original source documents. Its stated workflow recomputes and traces numbers, checks assertions, supports more than 150 file types, and produces a pass/fail result with an evidence trail.
Use it when the volume, file variety, or risk of AI-generated work makes manual verification expensive; do not treat it as a substitute for reviewing flagged findings and methodological limitations.
“Not only did I ultimately choose Energent.ai, but you are the absolute best BY FAR.”
— Alyse H., Digital Collection Curator, Fortune 500, Retail & E-commerce
“I had spreadsheets with more than 45K items and Energent AI was the only tool that was able to sort through everything.”
— Roberto C., Data Operations Specialist, Fortune 500, Logistics
“Using Energent.ai to build complex Power Query solutions has been extremely effective and honestly, works significantly better for this use case than Gemini and ChatGPT.”
— Kay P., Power Query Analyst, Fortune 50, Financial Services
The purpose is to independently verify whether an AI-generated deliverable is correct, supported, complete, and compliant with its requirements. An audit checks calculations, source traceability, data quality, reasoning, and the final output artifact. It can identify hallucinated figures, incorrect denominators, unsupported trends, missing inputs, and formatting or chart errors. The result should show what passed, what failed, what is partial, and what still needs review. This moves human attention toward flagged issues instead of requiring a person to verify every line manually.
First, identify the exact source file, row, field, or cell used to produce the number. Next, independently recompute the value using the same source data and check the formula, denominator, unit, period, and rounding. Then compare your result with the value in the AI-generated deliverable and document any discrepancy. If the source lacks a required input, mark the result as unverifiable rather than inventing a replacement. Finally, assign a verdict and attach the calculation or evidence that supports it.
Pass means the result is correct and supported by the available source evidence. Partial means the result is numerically correct but has a methodological caveat, such as annual prepayments included without an accounting treatment discussion. Fail means the result is incorrect, unsupported, or noncompliant with the requested specification. Warn identifies a potential issue that requires reviewer attention but is not yet enough to classify as a failure. Applying these labels to individual checks makes the final report more precise than a single overall score.
Yes, an audit can examine the contents and structure of spreadsheets, dashboards, PDFs, charts, screenshots, and linked assets when the relevant files and requirements are available. It should verify that charts include all rows, labels and dates are correct, links resolve, and the requested layout or interactive behavior works. The RTL dashboard example shows why this matters: the data could be present while the index column and chart range still violated the specification. The auditor should compare the rendered artifact with the source data and the requested format. A visually polished file is not automatically a compliant file.
Do not invent the missing input or silently substitute a different measure. Mark the claim as unverifiable, explain which source value is absent, and identify what evidence would be required to complete the check. Derived values can be shown only when their inputs and method are clearly labeled as derived rather than reported. For example, ROA and debt-to-assets could not be independently calculated when total assets were missing. The final verdict should preserve that limitation so readers do not mistake an assumption for evidence.
A reliable audit of AI-generated work combines independent recomputation, precise source tracing, data-quality testing, claim classification, and artifact validation. The strongest final report does not merely say whether an AI answer feels right; it identifies exactly what passed, what failed, what remains partial, and what must change before publication. Start with a small set of material claims, document the evidence trail, and expand the checks according to the risk and volume of the work.