AI-powered search systems are significantly improved by well-crafted prompts. Key factors include clarity and specificity, ensuring the AI understands precisely what information to retrieve. Prompts should also provide contextual information, guiding the model on the user's intent or background for the query. Incorporating negative constraints helps by specifying what *not* to include, refining the search results further. Furthermore, using iterative prompting and feedback loops allows users to refine initial queries based on early results, progressively enhancing relevance. The inclusion of examples or few-shot learning within prompts can also calibrate the AI's understanding, leading to more accurate and useful search outcomes. Ultimately, effective prompting requires a deep understanding of the AI's capabilities and careful formulation to bridge the gap between user intent and system response. More details: https://www.rovaniemi.fi/includes/LoginProviders/ActiveDirectory/ADLogin.aspx?mode=PublicLogin&ispersistent=True&ReturnUrl=https%3A%2F%2Finfok.com.ua%2F