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Understanding LLMsThrough Claude Code

Understand the structural constraints of LLMs and learn why designs look the way they do. The subject is Claude Code. The principles are not product-specific.

About this site ​

LLMs are not omnipotent. Transformer-based models have structural constraints: quality falls as input grows, information in the middle is missed, and content that contradicts facts is generated.

This site is a "bookshelf of Why." It is for learning those constraints and the design principles that transfer across prompt and agent design.

The intended reader is a developer who uses cloud LLMs daily. The goal is to understand the constraints and apply them in one's own environment.

Claude Code is the main subject because it is a representative example that can be described in detail and accurately today. The structural constraints and design principles here do not depend on a specific product. The same constraints appear in Cursor, Cline, or plain prompt design. The same way of thinking applies.

The destination is Part 11: Cross-LLM Principles. Parts 1–10 confirm the principles through Claude Code as a representative example. Part 11 then extracts what does not depend on the product.

Sister projects ​

Three sister projects can be read in order: "Know LLMs → Know Agent Design → Apply to Systems." This site is the "bookshelf of Why."

PhaseProjectFocus
1. Know LLMsThis siteLLM structural constraints and why designs look the way they do (bookshelf of Why)
2. Know Agent Designai-agent-architectureMCP, Skills, and Agent composition with implementation patterns (map of What/How)
3. Apply to SystemsManagement-of-software-systems-and-servicesComing soon — system operations in the AI era

TIP

After the Why is clear, read the sister site ai-agent-architecture for concrete implementation patterns (MCP catalog, Skills design, Agent taxonomy, A2A protocol, and so on). Why and How then connect.

Released under the CC BY 4.0 License.