In a recent LinkedIn post, Ruben Hassid offers a pragmatic approach for professionals struggling to keep pace with the rapid advancements in artificial intelligence. Rather than succumbing to the overwhelming flow of AI news and tools, Hassid proposes a structured method to effectively integrate AI into weekly workflows. His core message emphasizes focused adoption and iterative learning over trying to master every new development.
Hassid begins by acknowledging the universal challenge of staying current with AI. He writes:
“You can’t keep up with AI. Me neither.”
This candid admission sets the stage for his practical, step-by-step guide, designed to empower individuals to leverage AI tools without feeling perpetually behind.
Focusing Your AI Efforts
A central theme in Hassid’s advice is the necessity of strategic focus. He advises professionals to “pick your battle,” suggesting that attempting to learn and utilize every AI tool is an inefficient strategy. Instead, he recommends selecting a single AI model, such as ChatGPT or Claude, and identifying one recurring weekly task that could be enhanced by AI. The crucial next step, according to Hassid, is to clearly define what a successful outcome looks like for that specific task. This could range from generating a better email draft to completing a presentation or creating a usable spreadsheet.
Providing Effective Context for AI
Hassid stresses the importance of providing AI with sufficient context to produce desired results. He outlines this as the second key step in his framework. Simply asking an AI to perform a task is often insufficient. Hassid suggests uploading examples of work that align with the desired output and explicitly explaining the necessary tone, structure, or format to be emulated. He provides a concrete example:
“For eg. ‘Use this newsletter as a style reference. Turn my notes into a new edition for small business owners. Keep it under 500 words. Check every number against my notes.'”
This level of detail, Hassid argues, significantly improves the AI’s ability to generate relevant and high-quality content.
Iterative Learning and Exploration
The third pillar of Hassid’s strategy is iterative play and experimentation. He encourages users to actively engage with the AI by trying different prompts and exploring various models and features. This hands-on approach allows users to discover what works best for their specific needs. Hassid points out the value of saving effective instructions and prompts that consistently yield good results. He notes the importance of trying different models and features to understand the nuances and capabilities of various AI tools.
Upgrading Expectations and Connecting Tools
As users become more comfortable with one AI tool, Hassid suggests they can begin to “upgrade your expectations” by connecting different AI capabilities. He envisions a future where one AI might handle research, while another generates images, allowing for more complex workflows. The “pick one AI → play → iterate” pattern should be applied to new tasks and even different AI models. Crucially, Hassid advises users to carry their accumulated context—examples, preferences, and instructions—when switching between AI platforms.
“Keep your examples, preferences, and instructions. Bring that context with you when you switch.”
This persistent context, he implies, is key to maintaining efficiency and personalization across different AI applications.
Patience in a Fast-Paced World
Finally, Hassid emphasizes the virtue of patience. He acknowledges that while technology advances at breakneck speed, human adaptation is a more gradual process. He encourages readers to save his post for future reference and to engage with AI tools mindfully, rather than getting lost in the constant stream of hype. For those seeking to stay informed without the noise, Hassid recommends limiting AI news consumption to a couple of focused newsletters.
Ruben Hassid’s framework offers a grounded, actionable strategy for navigating the complexities of AI adoption, promoting focused learning and iterative improvement as the most effective path forward.
📝 About This Content
This article is based on insights shared by Ruben Hassid on LinkedIn.
📅 Originally posted on September 10, 2026 | View original post on LinkedIn →