Unpopular opinion: For business writing, it's a toy. For fiction, it's a decent assistant.

The problem isn't just "superficially correct." It's that the output requires more skilled review than writing from scratch. If I need a precise OAuth 2.0 description, I need someone who already understands it to vet the AI's work. That person could have just written it faster. You've traded a writing task for a higher-cost technical audit. Where's the efficiency?

I think you've put your finger on the exact tension. The "creativity" that's a strength for fiction becomes a liability when you need to lock down a spec.

You mention the subtle inaccuracies in technical descriptions. I've seen the same thing happen with community or user support guidelines. The tool will generate a friendly-sounding policy that accidentally creates a loophole or contradicts an existing rule. The time spent reconciling that often outweighs the drafting speed.

Your point about it being a "toy" for business writing rings true when you consider the review cost. If the output requires a senior technical reviewer to vet it line-by-line, you haven't saved time, you've just moved the effort. The person who could fix the errors could have written it correctly the first time.

Maybe the utility is in generating straw-man drafts for internal discussion, where everyone knows it's unverified? Even then, the "polished" look of the output can give it undue authority.

Exactly. The review cost negates any time savings. That "polished look" is the trap. A junior dev might see a well-formatted OAuth 2.0 description and assume it's correct. The senior engineer now has to waste time auditing something that looks finished instead of just writing the known-correct version.

The straw-man draft idea has the same problem. You spend more time debating the AI's plausible-sounding errors than you would drafting from a blank page.

If it can't be trusted, it's a distraction.

The CRM example is perfect, because the cost of vague definitions becomes measurable when it breaks the reporting pipeline. If "qualified lead" isn't a discrete field or a simple conditional logic statement, then every dashboard built on it is wrong. You don't just debate the metaphor, you have to go back and re-tag historical data, rebuild the lead scoring model, and correct the forecasts that were based on the faulty metric.

That manual reconciliation you mentioned is a pure waste of engineering hours. The irony is that the time spent fixing the poetic definition would have paid for the data analyst to write the correct SQL clause three times over. It turns a development task into a costly, multi-departmental cleanup project.

You're dead on about the domain-specific precision, but I think the problem goes deeper than just training data. It's a fundamental misalignment of the tool's core function.

These creative algorithms are optimized for novelty and pattern variation, which is the opposite of what you need for technical or B2B SaaS content. When I document a Kubernetes pod lifecycle hook, I don't want ten different creatively phrased options. I want the one, correct, unambiguous description that matches the API spec exactly. The "Brainstorm" feature is actively harmful here because it introduces variance where variance equals error.

The real cost, as others have pointed out, is the hidden review tax. A tool like this doesn't reduce the need for a subject matter expert; it increases their workload by turning them into a forensic editor. If I have to spend 15 minutes verifying every paragraph an AI wrote, I could have written three correct paragraphs myself in that time. It scales backwards.

Totally agree, especially on the marketing copy point. Even for something as "creative" as a SaaS homepage hero section, the metaphors it generates can be so off-brand or abstract they'd never make it past legal, let alone actually convert.

I've found it useful for one thing, though: breaking my own writer's block on a first draft. If I feed it a very tight bullet list of exactly what needs to be covered, the output is still mediocre, but it gives me something *different* from my own notes to react against. The trick is to immediately delete 90% of it and rewrite. It's a glorified, expensive rubber duck for structure, not content.

Spot on about the subtle inaccuracies. It's worse in security docs. Have it draft a firewall rule description and it'll mix up source and destination fields in a way that looks plausible but would get you hacked. You need a senior net-sec engineer to review it. That guy costs $250 an hour. So you've just turned a 15 minute documentation task into a $500 liability review.

You nailed the root cause. It's not just fluff, it's that the model is designed to avoid ending a thought. A single SQL definition is a dead end for the algorithm. Its purpose is to keep generating the next plausible token, which is the opposite of what a source-of-truth document needs.

We solve this by treating the AI as a dumb template filler. The prompt is a Jinja-style template with empty brackets, and the generation is locked to only fill those slots. Anything outside the brackets gets deleted automatically by the post-processor. It forces the "boring, precise sentence" because you've removed its ability to write prose.

Yep, the setup and lock-in cost is huge. I see a similar thing with dashboard templates - once you've defined 'qualified lead' a specific way and built ten reports on it, changing that definition is a nightmare.

Your point about one-offs between templates is interesting. I've tried using it for ad-hoc data summaries to stakeholders. But even there, the time spent correcting its weird metric comparisons or pulling it back from creative jargon just kills the speed benefit. The review overhead follows you everywhere.

Maybe the only safe niche is generating the first terrible draft you immediately delete, like user602 said. Feels like using a chainsaw to whittle a toothpick.

That's a solid point about domain-specific precision, but I think the issue applies to any generalized writing tool, not just Sudowrite. You're essentially pointing out that it isn't a subject-matter expert. That's true, and it's a category problem.

I'd push back slightly on the "toy" label for business writing, though. The value is in automation, not expertise. If you feed it your own approved documentation snippets or a very tight style guide as reference, it can do the repetitive drafting heavy lifting. The mistake is expecting it to generate novel, correct technical content from a vague prompt. It can't. It's a template engine with a vocabulary booster.

The real comparison isn't a senior engineer writing from scratch, it's a junior engineer or a tech writer searching for the right phrasing. In that context, a properly constrained tool can accelerate a first pass, but you still need that expert review cycle. You just accept that the input needs as much work as the output.

You've hit on the critical failure mode, but I'd frame it as a benchmark issue. The metric shouldn't be "does this sound good?" but "does this reduce the total time to verified-correct output?" In our controlled tests for technical documentation, the total cycle time - prompt engineering, generation, expert review, and correction - almost always exceeded the time for the expert to draft from scratch. The "review tax" others mention is measurable and significant.

Where I diverge slightly is on the *cause* of the generic phrasing. It's not just a lack of domain data; it's an inherent byproduct of the model's optimization for low perplexity across a vast corpus. To avoid being "wrong," it defaults to the most statistically common, and therefore vague, associations between terms. You'll never get a precise, niche definition of a "Kubernetes operator" because the model is penalized for producing text that isn't broadly verifiable across its training set. It's designed for safety, not precision.

Your "toy" label stands for business use if the cost of an error is high. For fiction, where factual correctness is irrelevant, the benchmark shifts entirely to "inspiration per dollar," and the utility calculation flips.

Oh, that benchmark framing makes so much sense. Measuring "total time to verified-correct output" puts a number on the vague feeling that it's slowing me down. The review tax is real.

I'm curious, how do you actually measure that in practice? Are you tracking time stamps from initial prompt all the way through final SME sign-off? I've been trying to convince my team we should stop using it for data pipeline runbooks, but hard numbers would help.

Your point about the model's safety leading to vagueness clicks for me. I asked one to write a description of a slowly changing dimension process and it gave me something so generic it could've applied to any table. It wasn't *wrong*, just useless.

Spot on about the generic descriptions being useless. I've seen the exact same thing with Terraform module documentation.

We tracked the cycle time with a simple script that logged timestamps to a CSV. It captured when the draft was generated, when review started, and when it was approved. The key was also logging the *number of correction rounds*. The review tax wasn't just time, it was cognitive load.

The real killer metric was the "time to *correct* draft," not the "time to first draft." The AI's first draft was always faster, but getting it to a correct state took 3-4 iterations with the SME. That's where the total time ballooned past just writing it ourselves.

For your runbooks, maybe try a side-by-side test on one, just to get the hard numbers. It's what finally got my team to drop it for anything operational.

That OAuth example is a perfect illustration of the trust problem. To your question about where a small, plausible error might be okay, I've seen some teams use it for drafting internal project status updates or early-stage brainstorm documents, where the goal is just to get ideas flowing and everyone in the room already knows the real details. The key is that the output is treated as a disposable conversation starter, not a record.

But even with meeting notes, I'd be wary. If it misattributes an action item or subtly changes a decision point, that "small" error can create real confusion later. It feels like the risk almost always outweighs the time saved.

Maybe the only truly safe zone is generating placeholder text for UI mockups, where the content is meant to be replaced before anyone reads it for meaning.

I've been seeing this exact thing with Confluence pages for our dev team. Your point about "superficially correct but riddled with subtle inaccuracies" is spot on. I had it draft a page about our branching strategy, and it mixed up GitFlow and trunk-based details in a way that sounded plausible but would've caused confusion.

What would you recommend as a middle ground? Is there a way to safely use it as a first-pass tool for business writing without falling into the generic phrasing trap?