In a recent LinkedIn post, Ruben Hassid explores advanced strategies for interacting with AI models like Claude, moving beyond simple prompt commands to unlock significantly more powerful and personalized outputs. Hassid frames the progression through seven distinct levels, each building upon the last to leverage the AI’s capabilities more effectively.
Hassid begins by establishing a foundational principle for enhanced AI interaction, emphasizing a shift from basic instruction-giving to a more conversational and guided approach. He notes that many users remain at a rudimentary level of engagement. According to Hassid, the core of this initial advancement lies in a simple yet impactful phrase added to prompts.
“End every prompt with ‘Ask me questions first.’ Claude interviews YOU. You click the answers. The output gets 10x more personal.”
This technique, Hassid explains, prompts the AI to seek clarification and context directly from the user, leading to outputs that are far more tailored than generic responses. This sets the stage for deeper engagement.
From Typing to Talking: Enhancing Prompt Efficiency
Hassid then delves into methods for increasing the speed and richness of prompt creation. He points out the significant difference in speed between typing and speaking, advocating for voice input to create more detailed prompts in less time.
As Ruben Hassid highlights, speed is a critical factor in efficient AI use: “You type 60 words a minute. You speak 150.” He recommends using tools like the Wispr.ai app to facilitate this voice-to-prompt workflow. This approach, he argues, allows users to generate more comprehensive and nuanced prompts, saving valuable time in the process.
Leveraging AI for Complex Tasks with the Claude App
Moving to higher levels of interaction, Hassid discusses how to utilize the Claude application more effectively for complex tasks, particularly by moving beyond the standard chat interface. He introduces the concept of using the “Cowork” tab within the app to process larger amounts of information.
“Get the Claude app → open the ‘Cowork’ tab. Drop in a messy doc, old proposal, numbers. Then: ‘Build a spreadsheet to do [X]. Ask me what you need first.’ 3 minutes later: a real Excel file.”
This method, according to Hassid, transforms the AI from a simple text generator into a powerful tool for data manipulation and organization, capable of producing structured outputs like spreadsheets from unstructured input.
Connecting AI to Your Workflow and Personalizing Skills
Hassid further elaborates on integrating AI into a user’s existing digital ecosystem. He explains the functionality of connecting the AI to personal applications such as Gmail and Calendar.
According to Ruben Hassid, this integration allows for highly personalized and context-aware assistance: “Prep me for tomorrow using my emails, calendar, and meeting notes.” This capability, he suggests, moves the AI from a standalone tool to an active participant in managing a user’s daily professional life.
A significant advancement highlighted by Hassid is the ability to teach the AI specific, recurring tasks, thereby creating custom skills. He outlines the process using the `/skill-creator` function:
“Type /skill-creator and prompt: ‘Teach Claude to build spreadsheets exactly the way I like them. Ask me questions first.’ Answer its questions. Click ‘Save skill.’ Now /[Skill name] works in every future chat.”
This feature, Hassid argues, allows users to institutionalize their preferences and workflows, ensuring consistent and precisely tailored AI output for repetitive tasks.
Optimizing AI Performance with Model Selection and Advanced Features
Finally, Hassid addresses the crucial aspect of selecting the right AI model for the task at hand. He points out that different models have varying strengths and that using the wrong one can be inefficient.
Ruben Hassid emphasizes the importance of strategic model choice: “The wrong model wastes the exact same prompt.” He suggests using lighter models for quick tasks and more powerful ones, like Opus, for demanding computations. Furthermore, he introduces the concept of “Vibecode” using the
Throughout his post, Hassid reiterates that the fundamental principle of asking the AI to ask questions first underpins all these advanced techniques, making AI interaction more effective and personal.
📝 About This Content
This article is based on insights shared by Ruben Hassid on LinkedIn.
📅 Originally posted on July 16, 2026 | View original post on LinkedIn →