Make your AI assistant predict the result before it runs it
Less technical than my usual posts. Nothing to install, nothing to pay for, and it works the same whether you're using an AI assistant for code, spreadsheets, research or writing. Here is the shape of the problem I want to describe. See if you recognise it. You ask your assistant to do something that produces a result. It does. A number, a summary, a status, an answer comes back. It's plausible.…
In today's world, AI assistants are becoming increasingly popular. They can help with various tasks such as coding, spreadsheets, research, and writing. However, a common issue with these assistants is that they can provide incorrect results due to misunderstandings or misinterpretations of the user's request. This can lead to significant problems and costly mistakes.
To address this issue, a new technique has been proposed that can be implemented without any additional costs or installations. Before running any task that produces a result, the user should ask the AI assistant two critical questions:
1. What do you expect the result to be, and why?
2. How would you know if this were broken?
By asking these questions before the assistant runs the task, the user can ensure that the assistant is solving the right problem and has a clear understanding of what the expected outcome should be. This technique can help prevent structural failures that can occur when the assistant produces plausible but incorrect results.
The first question changes the nature of the problem the assistant is solving. Instead of being an open-ended task of explaining a result that has already been seen, it becomes a closed task of predicting the result before it is even generated. This closed task allows the assistant to provide a confident explanation that can be verified as correct or incorrect. The second question is equally important as it forces the assistant to provide a statement about the machinery that must hold true regardless of the final result.
Implementing this technique can have significant benefits. It is cheap to produce and impossible to fake in hindsight, making it an excellent tool for ensuring the reliability of AI-generated results. Additionally, this technique can help prevent the common failure of structural issues where the assistant measures something that was not asked for or reads an empty file.
However, it is essential to note that predicting the result does not guarantee that the question was asked correctly. If the setup is broken, the prediction and result can be wrong in the same direction, agreeing with each other perfectly and confirming nothing. Therefore, it is crucial to continue testing and verifying the results, even after implementing this technique.
In conclusion, by asking the AI assistant two critical questions before running any task that produces a result, users can significantly reduce the chances of incorrect or misleading results. This technique is easy to implement and can lead to more reliable and accurate AI-generated outcomes.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
