I've been conducting a systematic analysis of various AI writing assistants as part of my vendor risk assessment workflow, with a particular focus on their utility for drafting and refining security policies, compliance documentation, and contract clauses. A recurring point of frustration I've documented is ChatGPT's "rewrite for clarity" function, which, in my controlled tests, frequently degrades the original text rather than improving it.
My methodology involves taking a precisely drafted source text—often a paragraph from a SOC 2 Type II report description or a GDPR Article 28 data processing agreement clause—and submitting it to ChatGPT with the instruction to "rewrite this for clarity and conciseness." The issues I've cataloged are consistent:
* **Loss of Precision:** Technical and compliance terminology is replaced with more common but less accurate language, stripping the text of its necessary specificity. For example, "implemented logical access controls to enforce the principle of least privilege" becomes "set up system rules to limit user access," which is materially weaker for an audit context.
* **Introduction of Ambiguity:** The rewritten version often adds vague qualifiers like "generally," "effectively," or "various," which would be flagged as unacceptable in a formal security or contractual document.
* **Structural Disruption:** In longer passages, the logical flow of an argument or a procedural description is often reorganized in a way that damages the causal or sequential relationships between points.
* **Passive Voice Proliferation:** Ironically, in an attempt to sound more formal, it will often convert clear, active-voice sentences into passive constructions, which is the opposite of most clarity guidelines.
I initially hypothesized that my negative results were due to the highly technical nature of my source material. However, I expanded my test to include more general business communication, such as project status updates or procurement guidelines, and observed similar, though slightly less severe, patterns. The tool seems to prioritize syntactic rearrangement over a genuine understanding of the text's intent and required rigor.
My central question for the community is whether this aligns with your experiences, particularly for those using ChatGPT in professional or technical writing capacities. I am specifically interested in:
* The types of source material where you've observed this degradation.
* Any prompt engineering techniques you've found that mitigate the issue (e.g., specifying "do not alter technical terms," "maintain an active voice").
* Comparisons with other AI writing tools in your stack on this specific "clarity rewrite" task.
I am compiling a comparison matrix on this functionality, and your experiential data would be invaluable for a more robust assessment.
RTFM — then ask for the audit
You're describing exactly what I've run into. I work with B2B software docs, and that "loss of precision" is a real problem. It strips out the specific terms that matter to people who know the field.
I've found that giving it more specific instructions can help a bit, like telling it to keep the original technical terms intact. But you're right, it's unreliable. Have you found any other tools that handle this kind of text better?