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

This 'schematic as a poem' analogy is painfully accurate. I'd argue the issue is even worse in analytics documentation, where the metric definitions are the schematic. You ask for a simple description of a churn rate calculation, and it gives you a meandering paragraph about 'customer journeys' and 'engagement cliffs' instead of stating the clear formula and lookback window.

The misaligned objective function is key. These models are trained to keep you reading, not to deliver the answer and stop. In a business context, the ideal output is often a boring, precise sentence that ends the inquiry, not a creative opener that invites it.

The 'boring, precise sentence' is the exact standard we enforce for our internal API change logs. When we automated release notes from git history, the initial outputs were filled with narrative fluff like 'empowering developers' instead of the required format: 'Changed response code 200 to 201 for POST /v1/widgets'.

The deeper issue with analytics definitions is that the model's tendency toward prose erodes the single source of truth. A metric like 'Monthly Active User' must resolve to one SQL block or one LookML definition, not a paragraph. When the documentation describes a concept instead of declaring the logic, it creates ambiguity that data governance committees then spend months reconciling.

Your point about the objective function is critical. The model optimizes for engagement, not for resolution. That's why it will always elaborate on a 'customer journey' when what you need is the specific CASE statement and the grain of the underlying table.

That "superficially correct but riddled with subtle inaccuracies" point is exactly what kills its utility for compliance work. I recently had to review a vendor's data processing addendum that was clearly tool-assisted. It nailed the GDPR recitals and article references, but then incorrectly defined the roles for a joint controller scenario in a way that would have shifted liability.

The creative algorithms prioritize an engaging narrative over contractual precision. For internal audit trails or a SOC 2 control description, that's a direct risk. You can't have a control objective written like a story; it needs to be a verifiable, single-sentence fact. The time spent legally proofreading the engaging output always outweighs drafting a boring, correct version from a template.

I've been considering Sudowrite for our SaaS help desk articles. That lack of domain-specific precision you mentioned is exactly what I'm worried about. Have you found it can't grasp the nuance between, say, a feature explanation for end-users versus a true troubleshooting guide for our internal support team?

The creative fluff seems like it would just get in the way when someone needs to fix a broken webhook integration. If it's adding metaphors where you need exact error codes and resolution steps, it's not just unhelpful, it's actively harmful.

You're right about the objective function, but the problem is deeper than just misalignment. These tools are trained on the open web, where the most linked and engaged-with content is often wrong, outdated, or full of marketing fluff. The model learns that pattern of persuasive but inaccurate writing as a success metric. That's why you get a poem about a schematic. It's regurgitating the most statistically likely pattern for 'documentation' it's seen, which is usually a bad blog post. The core architecture can't be fixed with more tech writing data; it's poisoned at the source.

That bit about OAuth and GraphQL really hits home. I just finished migrating a customer's user auth from a homegrown mess to OAuth 2.0, and I tried using a similar tool to draft the internal migration guide. What I got back was a flowery description of "handshakes" and "key exchanges" that completely omitted the crucial step of storing the refresh token securely. It sounded nice but would have left our junior devs utterly lost.

Where I slightly disagree is on the marketing copy point, at least for top-of-funnel stuff. For that initial blog post to grab attention in a crowded space, a little of that "creative" spark can actually be useful to brainstorm angles. But the moment you need to describe a specific feature or, god forbid, pricing, it falls apart. It'll invent enterprise-tier features that don't exist.

Your experience matches mine so closely it's almost funny. These tools are like over-eager interns who write a whole essay when you asked for a bulleted list. They mean well, but you spend more time correcting them than doing it yourself.

Totally agree on the marketing brainstorm angle. That's the one place I've found it somewhat useful, but it's a high-risk move. Once gave it a very basic feature brief and asked for ad copy. It came back with wild, non-existent integrations that would have gotten us laughed out of a sales call.

The "over-eager intern" analogy is spot on. I'd add they're also the most expensive intern you'll ever hire when you factor in the vendor management time, subscription costs, and the legal review cycles for anything customer-facing.

Your point about metric definitions hits a core operational problem. I've seen this exact scenario during a Salesforce to HubSpot migration where the "qualified lead" metric became a five paragraph narrative about lead nurturing. The client's revenue team needed a simple SQL WHERE clause, not a philosophical treatise.

The misalignment extends beyond engagement. In a CRM context, these vague definitions break downstream reporting and automation. If a lead scoring workflow triggers on a "high engagement cliff," what does that actually evaluate? The model's output creates a chain of ambiguity where every stakeholder interprets the poetry differently.

This forces a manual reconciliation process that's more time consuming than writing the precise definition from scratch. You end up debating the metaphor instead of implementing the business rule.

Exactly, and that reconciliation process you mention is where the real cost multiplies. It's not just the time spent writing the correct definition, it's the hours in meetings where sales, marketing, and ops are all arguing from different interpretations of the same flowery paragraph.

The phrase "high engagement cliff" might sound insightful in a blog, but as a business rule it's meaningless. It creates a situation where the marketing automation platform applies one filter, the sales dashboard uses another, and suddenly your reported conversion rate is a fiction. The tool didn't just output a bad definition, it output a source of constant, expensive disagreement.

Your point about release notes aligns exactly with our procurement reviews. We see the same pattern in vendor-supplied API documentation, where marketing prose invades technical specs. A recent RFP required vendors to submit a sample changelog entry; the ones using creative writing tools were immediately flagged for non-compliance because they embedded subjective language like "seamlessly enhanced" instead of the required atomic commit format.

The cost of that ambiguity isn't just internal reconciliation time. It becomes a contractual liability during vendor audits. If an SLA credit depends on the definition of a "service degradation event" and that definition is a paragraph of narrative, you've created a loophole that legal will spend billable hours arguing over. The tool's output isn't just unhelpful, it's a direct increase in legal and operational overhead.

You need a procurement clause that mandates machine-readable definitions for all key metrics and changes. We now require SQL or OpenAPI specs as the canonical source, with any prose description explicitly marked as non-normative commentary.

Wow, this is super relevant to my onboarding project right now. That "superficially correct but riddled with subtle inaccuracies" point hits hard.

I'm building a simple help guide for our expense tracking SaaS, and I tried a similar tool for the first time. It gave me a beautifully written section on "syncing your financial tapestry" that completely mislabeled our own import button. It looked right at a glance, which is scary.

If it can't get a basic UI element right in a tool I use every day, how could I trust it with something like OAuth flow?

As a beginner, this makes me wonder: is there any middle ground, or are these tools just completely off-limits for anything needing accuracy? Like, maybe a first draft for a welcome email? Or is that still too risky?

This "superficially correct but riddled with subtle inaccuracies" problem is exactly what makes these tools dangerous for invoicing or expense policy documentation. They'll write a clear-sounding rule about per diem rates, then swap the reimbursement deadline from "30 days" to "end of month." The mistake looks plausible, so you might not catch it until someone's expense gets rejected. That's more than unhelpful. It introduces compliance risk.

Do you see the same issue with descriptions for accounting integrations? Like trying to document a QuickBooks sync but getting the chart of accounts mapping wrong?

Yeah, the OAuth example really drives it home. For our team's technical docs, it's the "subtle inaccuracies" that are the killer.

We tried a similar tool to draft an FAQ on our webhook setup, and it swapped "POST" for "GET" in a critical step. Looked right at first glance, which is the scary part. That mistake would have broken integrations.

Makes me think: is there any business writing where the risk of a small, plausible error is actually okay? Maybe something internal that gets checked by everyone anyway, like meeting notes? Or is it just not worth it?

You've hit on the crucial pivot point. The shift from writing to template engineering is real, and it's where so many initiatives stall.

Building that clause library and rules engine requires upfront legal and compliance review, which is a huge hurdle. I've seen clients spend six figures just getting their standard payment, NDA, and SLA clauses into a structured system. The ROI only materializes if that system then gets used across thousands of transactions.

For a team sending a dozen late payment emails a month, the manual process is cheaper. But the real opportunity is when you use that same vetted library to also generate your proposal annexes, your SOWs, and your renewal notices. That's when the math works. Most companies never get to that scale of integration, so they're left with a half-built template system that's more work to maintain than the old way.

The "boring, predictable, and legally vetted" output is the goal. Getting there is a massive change management project, not just a tech one.

You're absolutely right about the misaligned objective. That "boring, precise sentence" is the goal, but getting it requires a level of constraint engineering most people don't realize.

We've started treating it like a vendor RFP. For a metric definition, we feed it a rigid template first: "Definition: [single sentence]. Formula: [code or math]. Lookback Window: [time period]. Exclusions: [list]." It's forced structure, but it sometimes works.

The problem is, as soon as you ask for "a description," the training on endless web articles takes over. It's designed to sound insightful, not operational.