How I use LLMs to learn complex topics

Visual, simulation-based learning via LLM-generated code is more effective for complex technical topics than reading text-based summaries.

Key Points

Standard LLM explanations are often too simplistic or formatted as tedious bulleted lists, making them difficult to follow for complex topics.
The author proposes a simulation-based learning flow: using LLMs to build a foundational knowledge base, verifying its accuracy, and then generating a low-poly, interactive animation (similar to Rollercoaster Tycoon) to map concepts to visual objects.
This method was applied to create ChipTycoon, a simulation tracking the chip manufacturing process from sand collection to data center delivery.
Learning retention can be further enhanced by integrating 3D object transformations and adding interactive challenges or puzzles to the simulation.
The approach has been successfully applied to other complex systems, including rocket engines, LLM internals, F1 engines, and EUV lithography machines.
Workflow: Knowledge Base → Accuracy Review → Simulation Build → Deployment (e.g., GitHub Pages).
Mapping abstract concepts to tangible objects in a simulation reduces hallucinations and improves conceptual sticking power.