How I use LLMs to learn complex topics

Many engineers I know employ generative AI for various purposes, such as creating proof of concepts, internal tools, or dashboards, and even learning new subjects. However, I find the simplistic approach of LLMs in explaining complex topics to be challenging to follow. The excessive use of emojis can also be irritating. While investigating new AI bottlenecks that could delay data center construction, I realized there were several aspects of chip production that I was unfamiliar with.

Scanning the internet, I pondered whether there could be a game designed to guide users through the process of constructing a chip within a fab. This approach would undoubtedly be more effective, as it allows for the mapping of concepts to objects within the game. After giving it a try, the results were impressive. Rather than merely requesting AI to explain a subject, I utilize the following approach: the outcome is a visually appealing animation that is 100% accurate and free of hallucinations.

In my opinion, this method significantly surpasses the effectiveness of reading extensive materials on Google or attempting to comprehend a bulleted list generated by a language model. I have specifically implemented this method for learning about chip manufacturing and have launched it on the website ChipTycoon. Users follow a cart from the moment sand is collected until the chip is completed and delivered to a data center.

Although the low-poly design may not provide sufficient detail to visualize the quartz sand pile's journey after exiting the furnace, the simulation still serves as a useful indicator of the product's transformation through the various steps involved in manufacturing. To enhance the realism, I employ my skill in converting pictures into 3D objects, mapping the resulting objects to the simulation.

This allows for a more accurate design. Additionally, challenges are introduced to the simulation, which require users to answer questions about previous steps in the chip manufacturing process. This approach greatly aids in retaining the knowledge. Intuitive puzzles are also incorporated to further enhance the learning experience.