If you're a developer exploring AI coding assistants, you might have encountered Claude Code and wondered how it actually works under the hood. What's the relationship between the command-line tool you install and the large language model that powers it? How does the AI decide when to read your files or run commands? And how do those CLAUDE.md instruction files actually get interpreted?
Let me walk you through these questions by clarifying a fundamental distinction that often gets overlooked when people first encounter Claude Code.
The first and most important thing to understand is that Claude Code and Claude are two completely separate pieces of software working together. Claude Code is a command-line tool that runs locally on your machine. It's a client application you install, similar to how you might install Git or any other CLI utility. Claude, on the other hand, is the AI model running on Anthropic servers in the cloud.
Think of Claude Code as a web browser and Claude as a website. Your browser runs on your computer and sends requests to the website, which processes them and sends back responses. The browser then displays those responses and handles any interactions you have with the page. They're working together, but they're distinctly different pieces of software with different responsibilities.
This architecture matters because it determines where the "intelligence" lives in the system. Claude Code itself doesn't have artificial intelligence built into it. It's a relatively straightforward program that acts as a coordinator and executor. All the understanding, reasoning, and decision-making happen in the Claude model, which resides on Anthropic's servers.
Let me trace through what actually happens when you type a command Claude, find all Python files that import requests in your terminal. Understanding this flow will clarify how the two systems work together.
When you run that command, Claude Code receives your input. Its first job is to package up your request into an API call to Claude. But it doesn't just send your raw question. The agent also sends information about what tools are available in your local environment. This is crucial because Claude needs to know what actions it can actually request on your behalf.
The API request includes your message asking for help finding Python files. It also includes detailed descriptions of tools that Claude Code has implemented locally, things like running shell commands, reading files, writing files, and searching through directories. These tool descriptions explain to the Claude model what each tool does, what parameters it needs, and when it might be appropriate to use each one.
When Claude receives this request, it can see both your question and the menu of available tools. This is when the language model does its reasoning. It analyzes your request and determines that to find Python files importing a specific library, it needs to actually search through your file system. It recognizes that there's a tool available that can accomplish this task.
Here's where the API design becomes elegant. Instead of Claude simply returning text that says you should run this command, it returns a structured response that explicitly requests the use of the tool. The API response includes a special format indicating that the model wants to call a specific tool with specific parameters.
For example, Claude might respond by saying it wants to use a shell command tool with a parameter like grep -r "import requests" --include="*.py". This isn't a suggestion written in conversational text. It's a structured instruction that Claude Code can parse programmatically and execute without any interpretation needed.
When Claude Code receives this response from the API, it parses the tool call request. It sees that the model wants to run a shell command, so the agent executes that command on your local machine. It captures the output, which might be a list of file paths where "import requests" was found.
Now Claude Code needs to tell the model what happened. It makes another API request, but this time it sends the conversation history along with the results of the tool execution. It's essentially reporting back with here's what you asked me to do, and here are the results I got.
Claude receives this new information and continues reasoning. At this moment, depending on the results, it may call another tool to gather more information, or it may have enough to give you a final answer. If it needs more tools, the cycle repeats. The model requests a tool, Claude Code executes it, the agent sends the results back, Claude analyzes them and decides what to do next.
This continues in a loop until the model determines it has everything it needs. At that point, instead of requesting another tool call, Claude sends back a regular text response. Claude Code displays this response in your terminal, and the task is complete.
The critical insight here is that Claude isn't actually executing anything directly. It can't touch your file system or run commands on your machine. It can only analyze information and make decisions about what should happen next. Claude Code is the one doing all the actual execution.
Think of Claude as a very knowledgeable advisor who's working remotely. You call this advisor and describe what you need help with. The advisor thinks about your problem and says, okay, I need you to go look in your filing cabinet and tell me what's in the folder labeled accounts. You do that and report back what you find. The advisor then says, great, now open the file called transactions.txt and read me the first line. You follow those instructions and report back. This process continues until the advisor has gathered enough information to provide you with the answer or solution you need.
Claude Code is you in this analogy, physically performing the actions. Claude is the remote advisor, making intelligent decisions about what information is needed but not actually handling files or running commands directly.
This brings us to an interesting question about how Claude actually decides when it needs to use tools versus when it can answer from its existing knowledge. The answer lies in the training process and the design of the API interaction.
During training, Claude was exposed to vast amounts of data that included examples of appropriate tool use. The model learned patterns about when tools are necessary versus when it can answer from its training data alone. It developed an understanding of the difference between questions about general knowledge and questions that require specific information from a particular environment.
When Claude receives a request along with tool descriptions, it performs a reasoning process. For a question like "what is Python?", it recognizes this is asking about general programming knowledge that it learned during training. It can answer this without needing any tools. For a question like "what Python files do I have in my project?", it recognizes this requires information about a specific filesystem that it doesn't have access to and cannot know without looking.
The tool descriptions that Claude Code sends in the API request act as a menu of possibilities. The model reads these descriptions and matches them against what it needs to accomplish. If there's a tool that fits the need, it constructs a structured request to use that tool with the appropriate parameters.
Now, let's discuss CLAUDE.md files, which add an interesting dimension to this architecture. These files enable you to provide project-specific instructions that guide the AI assistant on how to interact with your codebase. But where do these instructions actually get processed?
When you have a CLAUDE.md file in your project directory, Claude Code reads this file from your filesystem. But here's the key point that often surprises people: the agent doesn't try to understand or interpret the instructions in that file. It doesn't have the intelligence to parse natural language or understand conditional logic written in English.
Instead, Claude Code simply reads CLAUDE.md as text and includes it in the context when making API requests to Claude. The local tool is acting as a messenger delivering a letter. It can carry the letter and hand it over, but it doesn't read the letter and make decisions based on what's inside. That's the model's job.
Let's trace through a specific example to make this concrete. Imagine you write in your CLAUDE.md file: DON'T read file guideline.md until you work on implementing a new endpoint.
When you start working with Claude Code, it reads the CLAUDE.md file and packages it up as part of the system instructions that get sent to Claude through the API. When the model receives your request, it sees this instruction along with your actual task request.
Now, Claude, being a large language model trained to understand and follow natural language instructions, interprets what this means. It understands that there's a file called guideline.md, that there's a condition about when to read it, specifically related to implementing new endpoints, and that it should avoid reading this file in other situations.
As the model works on whatever task you've given it, it keeps this instruction in context. If you ask it to add a new user profile page, Claude reasons that this isn't about implementing a new API endpoint, so it won't request to read guideline.md. But if you ask it to implement a new POST endpoint for user registration, the model recognizes that this matches the condition you specified, and it would then request that Claude Code read the guideline.md file to get additional context for the task.
Understanding this architecture has several practical implications for how you work with Claude Code and write your CLAUDE.md files.
First, you can write complex, conditional, context-dependent instructions in CLAUDE.md because Claude is sophisticated enough to understand and follow them. You can use natural language with phrases like "when working on," "before you," "only if," and "make sure to" because the language model has been trained to understand these kinds of directives.
Your instructions can reference concepts that require understanding semantic context, like distinguishing between implementing a new endpoint, fixing a bug in an existing endpoint, and refactoring code structure. The model can make these distinctions because it understands the meaning of these phrases and can evaluate whether the current task matches the condition you specified.
You could even write more complex conditional logic like if the task involves authentication, first read security-guidelines.md, but if it's just adding a new field to an existing endpoint, you can skip it. Claude would interpret this and follow the appropriate branch based on its understanding of what task you've actually asked it to perform.
Second, understanding that Claude Code is lightweight and mechanical, while Claude holds all the intelligence, helps explain certain behaviors you might observe. The agent won't question or filter what the model asks it to do. If Claude requests to read a file, the agent reads it. If the model requests to execute a command, the agent executes it. The decision-making and judgment calls all happen on the model's side.
Third, this separation provides benefits in terms of privacy and efficiency. Your code and files never leave your machine unless Claude specifically requests to read something. Even then, only the specific content that's read gets sent to the API. The files themselves stay local. Claude Code remains lightweight and doesn't need to download massive AI models. And Anthropic can improve Claude without requiring you to update your Claude Code installation.
This client-server architecture, where Claude Code acts as a local executor and Claude acts as remote intelligence, represents a thoughtful design choice. It balances several competing needs in building AI-powered developer tools.
The architecture keeps the tool installation lightweight. You don't need to download gigabytes of model weights or have powerful hardware to run inference locally. The heavy computational work happens on Anthropic's servers, which are optimized for running large language models efficiently.
It enables rapid improvement of the AI capabilities. When Anthropic improves Claude or releases new versions, you immediately benefit without needing to update your local Claude Code installation. The intelligence is in the cloud, so it can be updated centrally.
It maintains reasonable privacy boundaries. Your files stay on your machine until the AI specifically needs to see them to complete a task. You're not uploading your entire codebase to the cloud. Only the specific files and command outputs that the model requests get sent through the API.
And it provides a clean separation of concerns in the software design. Claude Code handles the mechanics of interacting with your local filesystem and terminal. Claude handles the intelligence of understanding your requests and figuring out what needs to be done. Each component can focus on what it does best.
This architecture pattern isn't unique to Claude Code - it's become the industry standard for AI coding assistants. Tools like Cursor, GitHub Copilot, Aider, and Windsurf all follow this same fundamental pattern: a local executor (IDE extension or CLI tool) communicates with a remote LLM through an API. Even their configuration approaches are similar - Cursor uses .cursorrules files, Aider uses .aider.conf.yml, all working the same way as CLAUDE.md: the local tool reads them and sends them to the LLM, which interprets and follows the instructions. The widespread adoption of this pattern across the industry validates its effectiveness for building AI-powered development tools.
As AI coding assistants continue to evolve, understanding these architectural patterns becomes increasingly valuable. Whether you're using Claude Code, GitHub Copilot, or other AI development tools, many follow similar patterns of separating the local client tool from the remote intelligence.
The key insight is recognizing where the "smarts" live in these systems. The intelligence, the understanding, and the decision-making happen in the large language models. The local tools are coordinators and executors that bridge the gap between your development environment and the AI. Instructions you write in configuration files get interpreted by the AI model, not by the local tool.
With this mental model, you can write more effective instructions, debug unexpected behaviors more easily, and make better use of these powerful new development tools. You understand what's happening behind the scenes when you ask your AI coding assistant for help, and that understanding helps you collaborate with the AI more effectively.
