A suggestion has emerged that an effective way to reduce inaccurate ChatGPT answers is to add a phrase at the end of a prompt: "Before answering, first ask three clarifying questions you need."

TechRadar, an IT media outlet, reported on April 14 local time that the approach focuses on having ChatGPT first confirm conditions the user may have left out, reducing the need to revise answers multiple times.

The key is to prompt ChatGPT to ask again about a request's scope and conditions, rather than giving an answer from the start. ChatGPT generally responds in smooth and confident sentences, but it is not uncommon for it to answer with missing details or based on incorrect assumptions. In such cases, users typically correct parts of the response one by one or re-enter the request from scratch. The suggested approach shifts part of that burden to the model.

The added phrase is simple. Users can append "Before answering, first ask three clarifying questions you need" to the end of a prompt. At first glance, it may not look much different from filling in context after a wrong answer appears, but it can reach the desired result faster and more efficiently in practice. The key is that ChatGPT checks needed conditions before answering, instead of guessing missing context on its own.

The approach was introduced as particularly useful for tasks where conditions are often left out, such as travel itineraries, event preparation and meal planning. For example, if users ask for a "weekend trip plan", ChatGPT is likely to produce a fairly generic answer. But if users add a request for three questions, the flow changes, with ChatGPT first asking about conditions such as budget, preferred scenery and available travel time.

Dinner preparation is similar. ChatGPT will build its answer after first checking details such as dietary restrictions, the meal theme and the number of people.

The questions exchanged in this process are not complicated. The explanation is that such prior checks are needed to gather all necessary information from the initial stage without omissions.

The benefit of the approach is not limited to improving accuracy. It lets users start by clarifying needed conditions, instead of correcting mistakes one by one after receiving an answer. It may look slower because it adds a step, but overall work time can be shorter as repeated revisions decrease.

Another benefit cited was that users do not have to recall every condition perfectly from the start. Even if they miss important elements or overlook small conditions that affect the outcome, ChatGPT's questions can fill the gaps. It is closer to an approach that supplements necessary information through short question-and-answer exchanges, rather than packing all context into one long prompt.

The approach presented still requires a bit of extra effort at the start. Because users must answer three questions, it takes more work than throwing in a single prompt and getting results immediately. Still, the explanation is that this effort is a "purposeful" one that replaces repetitive revision work. The more accuracy matters, the more time it can actually save.

For users who often use ChatGPT but have had to repeatedly revise results, adding one line to a prompt could be the starting point for changing the conversation itself. By encouraging the model to check conditions the user left out first, it can reduce what has been cited as ChatGPT's weakness: "plausible but off-target answers."