Hey everyone,
I wanted to share a small technical detail from working on my content sites. When I started integrating AI-assisted writing, the biggest challenge wasn't generating content, but making it sound human. Initial drafts often felt flat, predictable, and frankly, a bit robotic.
My solution involved a few post-processing steps. First, a 'burstiness' pass. This isn't about randomizing, but analyzing sentence length variance and adjusting it to mimic human writing patterns, which are rarely uniform. I found targeting a standard deviation of 4-6 words in sentence length within a paragraph usually did the trick.
Then came a pass specifically for contractions. AI models often avoid them, making text sound overly formal. I built a simple replacement engine that identifies common opportunities for contractions (e.g., 'it is' to 'it's', 'they are' to 'they're') and applies them where grammatically appropriate. This significantly increased readability.
Another small thing was injecting specific numbers. AI tends to be vague. Instead of 'a lot of people,' I'd try to find a way to insert 'over 200,000 people.' It just feels more real.
Finally, a 'banned words' sweep. I maintain an evolving list of about 80 words and phrases that, to me, signal AI generation (e.g., 'delve into,' 'unlock the potential,' 'tapestry of'). The system flags these for manual review or suggests alternatives. You can see the effect of these processes on content like the weekly awards on aceju.com or the brewing guides on dreqo.com.
Happy to answer any questions about the specifics of these post-processing steps.