Slop Grenades Make Your Audience Do the Work

When a Quick Answer Becomes Someone Else's Problem

Imagine you're on the marketing team at a company preparing its annual report. You've asked department heads to share their biggest accomplishments, measurable results, and notable projects from the past year. The plan is to highlight these achievements in the report and use some of the material to update the company's website.

One department delivers a beautifully written, three-page summary. It celebrates innovation, operational excellence, meaningful partnerships, and a continued commitment to customer success. There are headings, bullet points, and a particularly enthusiastic conclusion about the company's bright future. It looks practically ready to publish.

There's just one problem. The document contains almost no actual accomplishments. There are no measurable results, specific projects, or examples of what the department did differently this year. You discover that someone fed a few meeting notes into AI and asked it to write a compelling annual summary. Now you're chasing down the details you originally requested, trying to figure out which claims are accurate and whether anything meaningful happened.

Congratulations, you've received a slop grenade. The term describes AI-generated work passed along without enough review or judgment, leaving the recipient to sort through the output and finish the thinking. Your colleague may have saved some time, but that work didn't disappear. It just landed on your desk.

Now imagine that same content making its way onto the company's website. Prospective customers encounter polished descriptions of improved services, enhanced capabilities, and exceptional results, but little information to help them understand what the company actually offers. Unlike your colleagues, visitors aren't obligated to chase down answers. They can simply leave.

As AI becomes part of more content workflows, this is a problem worth watching for. Generating professional-looking copy is easier than ever, but making it genuinely useful still requires some work. A few adjustments to how you prompt, review, and evaluate AI-assisted content can help ensure that the time you're saving isn't coming at your audience's expense.

Polished-Looking Content Hides Unfinished Thinking

One of AI's more impressive tricks is making an incomplete explanation sound remarkably complete. Give it a topic, a few bullet points, and a request for professional website copy, and you'll likely get something that looks ready for publication. It has a logical structure, polished language, and perhaps a few persuasive calls to action.

You might read through it, make a few adjustments, and feel pretty good about the result. Unfortunately, good writing can hide missing information.

Consider a service page that says, "Our service includes comprehensive onboarding support." If you're familiar with the company, you might know exactly what that means: two training sessions, assistance preparing documents, and access to a dedicated support portal.

A prospective customer could reasonably interpret that same sentence as a dedicated specialist helping their team throughout the first month. Neither interpretation seems particularly far-fetched, but the details that would help someone evaluate the service are missing.

This problem existed long before generative AI. Organizations have always struggled with vague messaging, internal terminology, and assumptions about what their audiences already know. AI makes it easier to produce more of that content, often with enough polish that the gaps become harder to notice.

When you're familiar with a subject, your brain automatically supplies missing context. You know how the service works, which exceptions apply, and what a particular phrase means internally. Someone encountering the page for the first time doesn't have those advantages, and that's where the slop grenade lands: your reader has to figure out what you left unsaid.

Give AI More Than a Topic

A common starting point for AI-assisted content is something along the lines of: "Write a professional summary of our department's accomplishments this year." It's a reasonable request if you're looking for inspiration, but less useful if you expect the result to accurately describe what your team achieved.

Without additional context, AI has to fill in the blanks. It may lean on familiar industry language, common business objectives, and assumptions about what similar companies accomplish. The result might sound like it belongs in your annual report, but it could belong in almost any company's annual report.

Before writing your prompt, consider what the content needs to accomplish. Who will read it? What are they trying to learn? What information do you already have that would help answer their questions?

For our annual report example, you might provide completed projects, improvements to service delivery, customer feedback, and measurable results. You could also explain which achievements matter most to your customers and stakeholders, and how the information will be used across the report and website.

Now AI has something meaningful to work with. You can also ask it to identify information that seems necessary but hasn't been provided, giving you a chance to investigate those questions before publishing.

In Better Answers Win in the Age of AI Search, I explored why specific information, grounded in how your organization actually works, is more useful than interchangeable marketing claims. That principle becomes especially important when generating content with AI. The tool can help organize and communicate your knowledge, but you still have to bring that knowledge to the process.

Don't Let AI Fill In Facts You Haven't Confirmed

Giving AI more context improves the starting point, but there's another problem worth watching for: its ability to make uncertain information sound convincing.

Let's return to our annual report. The department's AI-generated summary celebrates "streamlined operations," "improved customer satisfaction," and "significant gains in efficiency." These sound like worthwhile accomplishments, but what actually changed?

Perhaps the team introduced a new onboarding process, updated some internal tools, and received positive feedback from a handful of customers. Those could be legitimate achievements, but they don't necessarily establish that customer satisfaction improved or that the company became more efficient. Asking AI to make the summary more impressive could simply produce a more convincing version of those unsupported claims.

A more useful contribution from the department might look something like this, assuming the details have been confirmed:

In April, our client services team introduced a new onboarding process that allows customers to submit required documents before their first meeting. The process is now available to all new customers, although we're still collecting data on whether it has reduced setup time.

That isn't a complete annual report entry, but it gives the marketing team something valuable: a concrete accomplishment, an explanation of what changed, and a clear indication of what hasn't been measured yet. It could also become the foundation for a useful update to the company's services page.

When using AI to develop content, separate confirmed information from assumptions and open questions. Ask it to flag claims that need verification, and allow it to acknowledge missing information rather than inventing a convenient answer. Someone familiar with the subject still needs to check those facts, since AI is remarkably good at making sentences sound confident, even when it doesn't know what it's talking about.