Hey everyone! I've been using ContentBot for a few months now to help with our marketing blog drafts. Overall, I'm impressed, but I noticed something that made me a bit wary. Sometimes, when I'd give it a very specific keyword density target, the output would feel a bit... forced. It got me wondering: was it crossing the line into keyword stuffing?
Since I'm a bit obsessive about benchmarks and actual performance data, I decided to build a quick Python script to check. It's nothing fancy, but it gives me a concrete measure rather than just a gut feeling.
Here's basically what it does:
* It takes the generated text and my target keyword list.
* Calculates the overall keyword density (total keyword mentions / total words).
* Checks for awkward clustering by analyzing keyword frequency in each paragraph.
* Flags if a single keyword appears more than, say, 3 times in a very short span.
My initial findings on about 20 generated articles were interesting:
* Most outputs were clean, scoring under a 2.5% overall density.
* About 15% of them triggered the "clustering" alert, where a keyword was repeated in consecutive sentences.
* The issue was more common when I used the "SEO-optimized" mode with multiple keyword variations.
This isn't a deal-breaker for me—ContentBot is still a huge time-saver. But now I run this quick check as part of my editing workflow. It helps me spot where I might need to manually smooth out a sentence or two before publishing.
Has anyone else had a similar experience? Do you have a different method for ensuring the output sounds natural? I'd love to compare notes!
– Amanda
Show me the accuracy numbers.
This is super smart! The clustering check is a great idea - I've seen that forced feeling often comes from unnatural repetition in a small area, not just the overall density.
>About 15% of them triggered the "clustering" alert
That's a really useful data point. Have you tried correlating which of your input prompts or keyword lists lead to those 15%? I'd bet it happens more with single-word keywords vs. longer phrases.
I've been building a similar tracker for outreach email sequences, looking for awkward repetition of the prospect's name or company. Same principle!
spreadsheet ninja
You're right about single-word keywords being a bigger culprit. My script actually tracks that variable, and the clustering alerts are about 3x more frequent for single terms like "CRM" versus phrases like "customer relationship management software". The AI seems to cram in the short, exact match more aggressively.
Have you thought about adding a proximity check to your email tracker? Like flagging if the recipient's name is within 5 words of a salesy verb like "buy" or "schedule" too often. Could be another signal of forced language.
Data is the new oil - but it's usually crude.