Building a Mental Model AI: How I Created a Learning-Focused Chat Mode

July 2025

I was struggling to understand a complex distributed systems concept when I realized traditional learning approaches weren’t cutting it anymore.

The Problem

I’d been diving deep into Kubernetes networking architecture, and despite reading documentation, watching videos, and going through tutorials, I couldn’t build a solid mental framework. The pieces felt disconnected - I understood individual components but couldn’t visualize how they worked together or relate them to systems I already knew.

The breakthrough came when I realized I needed a different approach to learning. Technical documentation gives you facts, but it doesn’t help you build the cognitive scaffolding to truly understand and remember complex systems. I needed something that could create visual representations, use analogies I could relate to, and build knowledge progressively.

That’s when I decided to create a specialized AI chat mode focused entirely on building mental models.

The Mental Model Builder Approach

The Mental Model Builder chat mode is designed around how our brains actually learn and retain complex information. It follows specific principles:

Visual Learning First

Every explanation MUST include a Mermaid diagram. This isn’t optional - visual representations help encode information in multiple ways, making it easier to recall and understand relationships between components.

Structured Knowledge Building

The mode follows a specific output format with required sections:

Core Concept: The fundamental idea explained simply
Mental Model: Visual diagram and analogies
Real-World Mapping: How this connects to familiar systems
Progressive Complexity: Building from simple to advanced
Memory Anchors: Specific techniques for retention

Technology-to-Reality Mappings

This was the game-changer for me. Complex technical concepts become understandable when mapped to familiar real-world systems:

Async operations = Restaurant kitchen order system (orders come in, kitchen processes in parallel, meals come out when ready)
Load balancers = Hospital reception desk (distributes patients to available doctors based on capacity)
Container orchestration = City traffic management system (routes containers like traffic to optimal destinations)
Database sharding = Library system with multiple branches (data distributed across locations for efficiency)

The Automatic Organization System

One of the most valuable features is the automatic file organization. Every learning session gets saved to structured directories:

learning-notes/
├── concepts/
│   ├── distributed-systems/
│   ├── kubernetes/
│   └── databases/
├── visual-models/
│   ├── diagrams/
│   └── mind-maps/
└── practice-exercises/
    ├── scenarios/
    └── implementations/

This creates a personal knowledge base that grows over time. Instead of losing insights in chat history, everything gets preserved in an organized, searchable format. I can review previous mental models, build on them, and connect new concepts to existing knowledge.

How Visual Learning Transforms Understanding

The requirement for Mermaid diagrams in every explanation isn’t just aesthetic - it’s based on dual coding theory. When we encode information both verbally and visually, retention increases dramatically.

Here’s what I noticed after using this approach:

Faster pattern recognition - Visual patterns help identify similar structures in new systems
Better debugging - Mental models help predict where problems might occur
Improved system design - Understanding relationships leads to better architectural decisions
Enhanced recall - Visual and analogical anchors make information stick

The progressive complexity building is crucial too. Instead of jumping into advanced concepts, the mode builds understanding layer by layer, ensuring each foundation is solid before adding complexity.

The Mental Model Builder Chat Mode

You can get the Mental Model Builder chat mode from this GitHub Gist.

The Learning Transformation

Since implementing this approach, my ability to understand and retain complex technical concepts has improved dramatically. The visual diagrams help me see patterns across different technologies. The real-world analogies make abstract concepts concrete. The progressive building ensures I don’t skip foundational understanding.

Most importantly, the organized knowledge base means my learning compounds. New concepts connect to existing mental models, and I can quickly review and build upon previous understanding.

The automatic file organization turned out to be more valuable than I expected. Having a searchable repository of mental models means I can quickly refresh my understanding of concepts I learned months ago, or find connections between seemingly unrelated topics.

Key Learnings

Visual representation is non-negotiable - Every concept needs a diagram to be truly understood
Analogies accelerate comprehension - Real-world mappings make abstract concepts concrete
Progressive complexity prevents confusion - Build foundation before adding advanced features
Organization enables compound learning - Structured knowledge bases grow more valuable over time
Memory encoding requires multiple pathways - Visual, verbal, and analogical approaches together work better than any single method
Active file management creates lasting value - Automatic organization prevents knowledge loss

If you’re struggling to understand complex technical concepts or find yourself re-learning the same things repeatedly, try building a mental model approach. The combination of visual learning, structured organization, and real-world analogies might transform how you learn and retain technical knowledge.