I've been using Copy.ai for about six months now, primarily for generating blog outlines, social media snippets, and some ad copy variants. It's been a solid tool for ideation. Lately, though, I've been thinking more about the underlying data these models are trained on.
Specifically, I'm curious about the potential for plagiarism, or at least for generating content that feels overly derivative. We know these models are trained on a massive corpus of publicly available text from the web. Given my work in content personalization and analytics, I'm always mindful of originality and potential duplicate content flags.
My main questions for the community are:
* Has anyone done a systematic check (using a tool like Copyscape or Originality.ai) on longer-form content from Copy.ai? I've spot-checked short outputs and they've been fine, but I'm less certain about 1000+ word articles.
* Does the risk change significantly depending on the specificity of the prompt? For example, if you ask for something very generic like "a blog post about the benefits of email marketing," are you more likely to get a patchwork of existing content than if you provide a unique angle, data points, and a specific structure?
* How does Copy.ai's approach compare, in your experience, to other tools like Jasper or Writer on this particular issue? I'm less interested in which is "better" overall, and more in the comparative risk of output similarity.
I understand that the output is technically "new" text, but the training data source is a bit of a black box. For those using this in a commercial or client-facing context, how are you mitigating this risk? Do you treat all AI-generated content as a first draft that requires significant rewriting?