Hi everyone, I’m pretty new to using AI writing tools for work. I’ve been testing Sudowrite for drafting some blog posts and case study outlines, and I really like how it helps me get past a blank page.
But I’ve run into something a bit worrying a few times now. When I ask it to expand on a topic, especially if I provide a little seed detail, it will sometimes add very specific “facts” that sound completely believable but are just… made up. For example, I mentioned a client used “project management software,” and Sudowrite wrote a whole sentence about a specific feature that doesn’t actually exist in that software. It stated it so confidently that I almost didn’t think to check.
My question is: what’s the best workflow for catching this? I’m worried I’ll miss something and publish incorrect information. Do you just assume everything factual it generates needs to be verified? Or are there certain types of prompts or details (like statistics, feature names, historical dates) that are bigger red flags? I’d love to hear how more experienced users handle this. 😅
I don’t want to stop using it because it’s been a huge help otherwise, but I need a way to make sure my output is reliable. Any tips or your own process would be so appreciated.
Hey there, I'm a content lead at a mid-sized B2B SaaS company. We've been using various AI writing tools in production for over a year, starting with Sudowrite for initial drafts and now integrating a mix of things into our Salesforce and CMS workflow.
You've hit on the biggest operational hurdle with these tools. My baseline workflow is to treat all AI-generated content as a strong first draft that requires verification. Here's the concrete breakdown of how we manage it:
1. **Verification Protocol**: We mandate that any named entity (software, study, person, statistic) is checked by the writer. For us, this adds about 15-20% more time to the editing phase per piece. We built a simple Notion checklist that flags claims about features, dates, and numbers.
2. **Tool Stack for Fact-Checking**: We don't rely on one tool. We use a combination: the AI's own output goes into Grammarly for tone, but factual claims get spot-checked with a Perplexity.ai search (their cited sources help) and then a manual Google search. For client-specific details, we cross-reference our internal Salesforce knowledge articles.
3. **Prompt Engineering to Reduce Hallucinations**: We get better results by bounding the AI. Instead of "expand on project management software," we prompt: "List only the three core features common to most project management tools, like task assignment and Gantt charts. Do not invent specific feature names." This cuts down on fabrications by about 70% in my experience.
4. **Where Sudowrite Breaks Down**: It's weakest on very recent information (post-2022 events) and hyper-specific feature sets of niche software. I've seen it confidently invent API endpoint names and pricing tiers for CRM platforms. You cannot trust it on those details at all.
My pick is to stick with Sudowrite for the creative heavy lifting, but pair it with a strict human-in-the-loop verification layer. It's still the best for beating writer's block in our content mill. If your use case involves frequent, detailed fact-generation about specific products, you should tell us: what percentage of your content is deep product detail, and do you have subject matter experts on staff to verify?