Hey everyone,
I've been using Copy.ai for a few months now to help generate marketing copy and product descriptions, and overall it's been a huge time-saver. Lately, though, I've run into a recurring issue that's starting to affect our workflow: **the AI keeps inventing product features.**
For example, I'll feed it the basic specs for our CI/CD monitoring tool—let's say it tracks build times and failure rates. The output will sometimes claim the tool also does "predictive failure analysis" or "automated rollback suggestions," which are features we don't have (yet!). I then have to spend extra time fact-checking and editing it all out.
I love automation, but this adds manual correction back into the process. 😅
Has anyone else dealt with this? I'm looking for practical strategies. Things I've tried:
* Being hyper-specific in the prompt (e.g., "Only describe the features listed in the following JSON...").
* Using the "Tone & Style" guides more aggressively to stick to factual reporting.
* Shortening the input context, thinking maybe too much info causes hallucinations.
What's worked for you? Are there specific prompt structures or workflows within Copy.ai that help lock the AI to the facts on the ground? Maybe a way to prime it with a stronger "stick to this list" instruction?
Sharing a snippet of a prompt that actually works would be amazing.
Thanks in advance for any tips!
mattk
Keep shipping.
Hyper-specific prompts sometimes backfire - the model overcorrects and you get unusably rigid text. I've had better luck feeding it examples of what I *don't* want.
For your CI/CD tool, try a negative prompt like "Do not mention predictive analytics, automated rollbacks, or any feature not explicitly listed here." Follow it with the real feature list in a code block. It cuts down on the creative flair.
Beyond prompting, this is why I avoid closed platforms for core work. You're stuck with their model's tendencies. Have you looked at running a local LLM with a tighter prompt template? The setup cost is higher, but you control the hallucination knobs.
Every cloud has a dark cost.
The negative prompt approach works, but I've found diminishing returns. It's like telling a model "don't think of a pink elephant." The explicit mention can sometimes reinforce the concept in the latent space.
I run comparative benchmarks on prompt structures. For feature-locked descriptions, providing a strict XML-like template in the system prompt consistently outperforms negative instructions in the user prompt. The model treats it as a data-filling task rather than a creative one.
> You're stuck with their model's tendencies.
This is the real bottleneck. Local models give you control, but the performance/accuracy trade-off for marketing copy is steep. Have you quantified the output quality drop in your tests? I see a 30-40% coherence hit on Mistral 7B versus GPT-4 for this specific use case.
Numbers don't lie