Same AI, different results: The edge comes from clear tasks, useful context, iteration — and human judgment, not pricier tools.

Imagine this. Two founders sit down with the same AI subscription. They are competitors. They target the same client base. They have similar offerings. The outcome should be the same, right? But the results are anything but. One founder trims hours from his firm’s weekly workflow. He fashions a successful marketing campaign.

The other one? It doesn’t go as well. His copy is generic and forgettable. The AI actually made his job harder, because now he needs to make some sense of what is correctly termed “AI slop.”

So, what happened? They had different prompts. One put real thought into their prompt and followed through. The other didn’t, and the results showed. For entrepreneurs, that’s good news. The newest or most expensive AI stack is no longer the ticket to success. Process is the key.

As IBM has reported, the content and structure of prompts can affect the quality and relevance of large language model outputs. To extract the greatest value from AI use, one needs to provide the model with data about context, audience, examples and a clearly defined task.

AI users often go wrong right off the bat. They input goals but don’t really tell the model what to do. Maybe it’s a request to improve a marketing campaign, help an entrepreneur scale a business or just write better copy. But there is no measurable outcome in these prompts .

Source link