{
  "id": 45333,
  "title": "Make your AI assistant predict the result before it runs it",
  "url": "https://urgent.news/2026/08/02/make-your-ai-assistant-predict-the-result-before-it-runs-it",
  "topic": "science",
  "section": "Science",
  "published": "2026-08-02T14:20:27.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/gde03/make-your-ai-assistant-predict-the-result-before-it-runs-it-2abo"
  },
  "original_language": "en",
  "account": "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.\n\nTo 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:\n\n1. What do you expect the result to be, and why?\n2. How would you know if this were broken?\n\nBy 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.\n\nThe 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.\n\nImplementing 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.\n\nHowever, 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.\n\nIn 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.",
  "summary": "The article discusses the importance of establishing expectations and validation mechanisms before AI assistants produce results. By predicting the expected outcome and defining how the assistant would identify if the result is incorrect, users can create a structured approach to AI-assisted tasks. This strategy helps prevent structural failures, such as the assistant measuring the wrong thing or reading an empty file, as these issues can be identified before the assistant generates a response. The technique is cost-effective, requiring only a few seconds to implement, and can significantly improve the reliability of AI-generated results by ensuring that the standard of success is firmly established prior to executing the task.",
  "key_points": [
    "Ask AI assistant two critical questions before running any task.",
    "Predict expected result and explain why before computation.",
    "Second question forces assistant to state verification method."
  ],
  "editors_take": null,
  "illustration": "https://urgent.news/ill/45333.png",
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}