MyMapHow LLMs Work Concept Map
A visual concept map explaining how large language models work — covering training, transformer architecture, inference, key limitations, and real-world use cases.
Preview
“Create a concept map explaining how large language models work”
About the framework
Concept Map Framework for Explaining Complex Technical Topics
This template applies the concept map framework — a structured radial diagram connecting ideas to a central concept — to explaining how large language models work. Concept maps are distinct from mind maps in one important way: while mind maps radiate from a single idea, concept maps show meaningful relationships between nodes, making them ideal for technical education where the connections between concepts are as important as the concepts themselves.
The five branches cover the complete picture of an LLM system. Training Pipeline traces the path from raw text to a fine-tuned model via pre-training and RLHF. Architecture explains the transformer mechanism, tokenization, and context window constraints. Inference covers the generation process — sampling, temperature, and the forward pass. Limitations maps the honest constraints: hallucination, knowledge cutoff, and reasoning failures. Use Cases grounds the abstract concepts in concrete applications that make the technology legible to non-technical audiences.
This template is used by developers introducing colleagues to AI, educators teaching machine learning concepts, and product teams getting up to speed on the technology stack they're building on. Use the AI to expand any branch with more depth, update the content to reflect newer model architectures, or create a simplified version for a non-technical audience.
What's included
What you get
- 5-branch concept map of LLM fundamentals
- Training pipeline: pre-training to RLHF
- Architecture: transformers, tokens, context window
- Inference process step by step
- Limitations and real-world use cases
How LLMs Work Concept Map
Frequently asked questions
Common questions
Can I use this template to explain a different AI concept, like diffusion models or reinforcement learning?
Yes. Replace the central node with your topic and describe it to the AI: 'Create a concept map explaining how diffusion models work for image generation.' The AI will generate appropriate branches covering architecture, training, inference, limitations, and use cases.
How do I simplify this map for a non-technical audience?
Ask the AI to 'Rewrite this concept map for a non-technical business audience, replacing technical terms with plain language analogies.' For example, 'tokens' becomes 'word fragments', and 'transformer architecture' becomes 'the system that pays attention to context.'
Can I use this as a teaching resource or course material?
Absolutely. The concept map format works well as a visual syllabus or course outline. Each branch becomes a lesson topic, and the sub-nodes become the key points to cover. You can export the map and embed it in course materials or use it as a discussion guide.
How do I keep this concept map up to date as AI technology evolves?
Ask the AI to review and update specific branches: 'Update the Architecture branch to include multi-modal capabilities and tool use.' The map is a living document — updating it periodically is much easier than rewriting a long-form explanation.
How LLMs Work Concept Map
Free to start. No credit card required.