Your mental model for AI must evolve from "chatbot" to "agent manager." Systematically test specialized agents against base LLMs on standardized tasks to learn what can be reliably delegated versus what requires oversight. This is a critical skill for managing future workflows.
Most users re-explain their role and situation in every new AI conversation. A more advanced approach is to build a dedicated professional context document and a system for capturing prompts and notes. This turns AI from a stateless tool into a stateful partner that understands your specific needs.
Go beyond creating pretty pictures. The real power is using an AI to reason through the logic of visual communication. Prompt it to determine the best way to visualize a concept (e.g., flowchart vs. 2x2 matrix) and explain the trade-offs, turning it into a tool for strategic communication.
Instead of passive learning, the program starts with an active creation project: building a custom web app. This hands-on approach demystifies AI's creative power and provides a tangible tool from the very beginning, fostering a builder's mindset over that of a simple user.
The goal of testing multiple AI models isn't to crown a universal winner, but to build your own subjective "rule of thumb" for which model works best for the specific tasks you frequently perform. This personal topography is more valuable than any generic benchmark.
Many users know about AI's research capabilities but don't actually rely on them for significant decisions. A dedicated project forces you to stress-test these features by pushing back and demanding disconfirming evidence until the output is trustworthy enough to inform real-world choices.