AI language models can produce a well-structured technical report in seconds. The prose is fluent, the structure is logical, and the conclusions sound authoritative. The problem is that they can also be wrong — and the errors are not always obvious.

This is not a reason to avoid AI-assisted reporting. It is a reason to build a review process that catches the errors before they reach a customer, a regulator, or a production decision.

Here is what to check.

The most common types of error in AI-generated technical reports

Understanding the failure modes helps you review more efficiently. AI language models make predictable types of errors in technical contexts.

Hallucinated specifics. The model may cite a specific value, threshold, or regulatory limit that does not exist or is incorrect. These errors are particularly dangerous because they are presented with the same confidence as accurate information. A report that states "the EU Cosmetics Regulation requires a minimum stability period of 18 months" sounds authoritative — but that specific requirement does not exist in that form.

Correct method, wrong application. The model may correctly describe a statistical method but apply it to data where it is not appropriate. Applying a parametric test to non-normally distributed data, or using a control chart method designed for continuous data on attribute data, are examples. The method is real; the application is wrong.

Plausible but unsupported conclusions. The model may draw conclusions that are consistent with the data but go beyond what the data actually supports. "The results indicate that the formulation is stable under accelerated conditions" may be a reasonable inference — or it may be a significant overreach, depending on the study design and the data.

Outdated regulatory references. Regulations change. A model trained on data from two years ago may cite superseded guidance, withdrawn test methods, or regulatory limits that have since been revised.

Internally inconsistent numbers. In a long report, the model may state a value in one section and use a different value in another. Tables and body text may disagree. Summary conclusions may not match the detailed findings.

A practical review checklist

When reviewing an AI-generated technical report, work through these checks systematically.

Check every specific value against a primary source. Any number that matters — a regulatory limit, a test method parameter, a specification threshold — should be verified against the original source document. Do not assume the model has it right.

Verify that the statistical methods are appropriate for the data. What is the distribution of the data? Is the sample size sufficient for the method being used? Are the assumptions of the test met? If the report uses a method you are not familiar with, look it up before accepting the conclusion.

Trace every conclusion back to the data. For each conclusion in the report, ask: what data supports this? Is the inference reasonable, or does it go beyond what the data shows? A conclusion that cannot be traced to specific data points should be flagged.

Check for internal consistency. Read the report with a focus on numbers. Do the values in the tables match the values cited in the text? Do the summary conclusions match the detailed findings? Inconsistencies are a sign that the model has generated text in sections without maintaining a coherent thread.

Check regulatory references against current versions. If the report cites a regulation, directive, or guidance document, verify that the version cited is current. Check the effective date and whether any amendments have been issued since the model's training data was collected.

The specific challenge of cosmetic safety reports

Cosmetic safety reports (CSRs) are a particularly high-stakes context for AI-assisted reporting. They are required by the EU Cosmetics Regulation for every cosmetic product placed on the EU market, and they must be prepared or reviewed by a qualified safety assessor.

AI can be useful for drafting the structured sections of a CSR — the ingredient inventory, the toxicological profile summaries, the stability data summary. But the safety conclusion — the assessor's professional judgement that the product is safe for its intended use — cannot be delegated to an AI model.

The errors that matter most in a CSR are not grammatical. They are errors of scientific judgement: an incorrect assessment of a toxicological endpoint, a missed interaction between ingredients, an inappropriate extrapolation from animal data to human exposure. These require a qualified human reviewer with domain expertise.

Building a review workflow

The goal is not to re-do the AI's work from scratch. That defeats the purpose of using AI assistance. The goal is a targeted review that catches the errors that matter.

A practical workflow:

1. AI drafts the report from structured data inputs (test results, formulation data, process parameters).
2. Automated checks flag obvious inconsistencies: values that fall outside expected ranges, missing required sections, regulatory references that do not match a current database.
3. Expert review focuses on the conclusions, the statistical methods, and any regulatory claims. The reviewer is not reading for grammar — they are reading for scientific accuracy and appropriate inference.
4. Sign-off by a named qualified person, with a record of what was reviewed and what was changed.

This workflow is faster than writing the report from scratch, more reliable than accepting the AI output without review, and produces a defensible audit trail.

What "human evaluation" actually means

When we say that every report we produce is reviewed by a PhD specialist, we mean something specific. The reviewer is not checking spelling. They are asking:

• Is this conclusion supported by the data?
• Is this method appropriate for this data?
• Is this regulatory reference current and correctly applied?
• Would I be comfortable defending this conclusion to a customer or regulator?

That last question is the most useful test. If the answer is no — if there is any doubt about whether the conclusion is defensible — the report goes back for revision.

AI makes the drafting faster. Expert review makes the output reliable. Both are necessary.