Evaluating Generated Text Quality
AI-assisted writing tools are applications that use AI to help users improve their writing, and they range from autocorrect and auto-completion to full content generation.
A Prediction Before Reading On
Many people have a fixed picture of AI writing in their heads before they ever open one of these tools. This article starts with a prediction so you can test that picture against evidence. Commit to an answer before you read the reveal.
What do you think happens?
Suppose a group of working professionals is assigned writing tasks specific to their jobs, and half of them are randomly given access to ChatGPT while the other half are not. What would you expect to happen to the time they need and to the quality of what they produce?
Reveal answer
Time goes down and quality goes up. The measured result from one MIT study showed that exposure to ChatGPT reduced time by 40% and raised output quality by 18% among the professionals who used it.
The result is a measured outcome, not a claim that AI removes the need for people. The professionals still produced the final text themselves, which is why the rest of this article asks what a person remains responsible for.
Two Popular Pictures and the Middle Ground
The first popular picture treats AI as a glorified spellchecker that fixes typos and nothing more. The second treats AI as a machine that produces a finished essay, email, or book from a single instruction, with no human involved and no judgment needed. Both pictures contain a grain of truth. Both leave out most of what actually happens when people write with AI today.
Where AI Help Sits on the Range
AI-assisted writing tools are applications that use AI to help users improve their writing, and they range from autocorrect and auto-completion to full content generation.
The source identifies several forms this help takes. Autocorrect and auto-completion are the most familiar, and both are powered by AI on a typical smartphone. Autocorrect repairs words as you type them. Auto-completion predicts the next words so you can accept them with little effort. These features sit close to the small end of the range because they handle fragments of text rather than whole documents.
Further along the range are tools that suggest rewrites of sentences or paragraphs, and tools that generate content from a brief. The amount of drafting the AI does grows as you move along this order, and so does the amount of checking a person must do before the text is trustworthy.
Several everyday applications already include this kind of help. Note-taking and email apps such as Google Docs, Notion, and Gmail use AI to help users improve their writing. Grammarly, a writing assistant app, fine-tunes a model so that a user's writing becomes more fluent, coherent, and clear. Customer relationship management apps such as HubSpot and Salesforce include tools that generate web content and outreach emails for enterprise users. Real products often combine several points on the range, so the scale is best read as a map rather than a strict set of boxes.
Who Uses AI-Assisted Writing
The uses of AI-assisted writing fall into several groups, and the needs of each group differ. The same tool can be valuable for one person and mostly irrelevant for another. The source divides users into individual users, students and writers, and organizations.
- Consumers and individual users, who use note-taking and email apps and writing assistants such as Grammarly to make their everyday writing more fluent, coherent, and clear
- Students and writers, who use these tools to improve their writing; the source notes that they remain responsible for the final text they submit or publish
- Enterprises, which use customer relationship management apps such as HubSpot and Salesforce to generate web content and outreach emails
Tracing a Draft From Prompt to Publication
Follow a piece of AI-generated text through its life. A person gives a brief, the AI produces a draft, and the draft is either checked or not before it reaches readers. The one step that separates a responsible use from a risky one is whether a person reads, edits, and decides whether to use the text.
Generated Guidebooks Flooding a Marketplace
The source describes a case reported in 2023 by the New York Times, in which Amazon was flooded with shoddy AI-generated travel guidebooks. Each guidebook came with an author bio, a website, and rave reviews. Why did these signals fail to show that the books were trustworthy?
- 1Identify the signals of credibility
Each guidebook had an author bio, a website, and enthusiastic reviews. These are the things a buyer would normally use as evidence that a book is real and has been checked.
- 2Trace where each signal came from
According to the source, the author bio, the website, and the reviews were all also AI-generated. The appearance of a credible author and a supporting web presence was produced by the same kind of tool that produced the book.
- 3Ask who actually reviewed the text
A guidebook that looks finished may contain content that no one verified. Polish and volume are weak evidence of quality when generation is cheap and checking is skipped.
The signals looked like independent evidence but were generated by the same process as the book. The lesson is to ask who has actually reviewed the text, not to accept how finished it appears.
Mistakes That Make Generated Text Risky
Common mistake Treating AI writing tools as only trivial spellcheckers
What it looks likeAssuming that an AI tool offering full drafts is just fixing typos.
Why it failsThe range runs from autocorrect to full content generation, and the source notes that the spellchecker picture leaves out most of what happens when people write with AI.
Do this insteadLocate the tool on the range and ask how much of the text it drafted.
Common mistake Treating AI as a machine that needs no human judgment
What it looks likePublishing a generated email or essay directly because a single instruction produced it.
Why it failsIn every user group the person remains responsible for the final text, and the decision to use the text belongs to a human.
Do this insteadRead, edit, and decide before the text is used, and own the result.
Common mistake Judging quality by polish and volume
What it looks likeA guidebook with a bio, a website, and glowing reviews is assumed to be reliable.
Why it failsThe source shows these signals were generated by the same kind of tool, so they carry little independent evidence.
Do this insteadAsk who has actually reviewed the text and whether the claims were verified.
Common mistake Skipping review because generation is fast and cheap
Practice: Who Reviewed This Text
A small company publishes a product guide written entirely by an AI tool from a one-line brief. The guide includes an author bio, a company website, and customer reviews that all praise it. Using the ideas in this article, identify which parts of this evidence are weak, which question you would ask first, and where in the prompt-to-publication path the review step should have happened.
Hints
- Ask which steps in the path involve a person reading, editing, and deciding.
- Consider whether the bio, website, and reviews were produced by the same process as the guide.
- Remember that polish and volume are weak evidence when checking is skipped.
Key Takeaways
- AI writing tools are neither trivial spellcheckers nor fully autonomous authors; they sit on a range from autocorrect and auto-completion to full content generation.
- The amount of drafting an AI tool does grows along that range, and the checking a person must do grows with it.
- Consumers, students and writers, and enterprises all use these tools, and in each case the person remains responsible for the final text.
- Generated polish, volume, and apparent praise are weak evidence of quality, because the same tools can produce them.
- Unreviewed AI-generated output is a risk, so the key question is always who has actually read, edited, and decided to use the text.