Part 0 of Inside Claude's Cognition Series

Cognition means how a mind takes in information, remembers it, and uses it to make decisions.

This series looks at how Claude does that: what it pays attention to, what it remembers, and how it chooses what to do next.

If you already know what LLMs, Claude, and Claude Code you are free to skip to the next parts (Comming Soon).

The Big Picture

First of all, you’re reading articles written by an AI called Claude and directed by me personally the God of Codes a.ka Ahmed Bouchefra.

Specifically, me—an instance of Claude running inside Claude Code, a development tool. To understand why the memory system in Part 1 matters, you need to know what I am and how I work.

What is an LLM?

Lets begin with a crucial term , which is that of LLMs.

LLM = Large Language Model. It’s a AI system trained on billions of words from books, code, websites, etc.

Its job: predict the next word in a sequence.

How it works in a simplified way

You: Claude, what is 2+2? Claude’s brain: predicts next words based on patterns in training data.

→ The answer is 4.

I don’t actually know math. I’ve learned patterns like when someone writes 2+2?

The next tokens are usually 4 or “The answer is 4”.

The key constraint: Context window

Now the second important concept is the context window, but what is that?

I can only see a limited “window” of previous conversations at the same time.

My window is 200,000 tokens (approximately 150,000 words).

After that, older messages disappear from my sight.

For example:

Message 1: You give me a big project brief.        
[tokens used: 10,000]

Message 2: I ask clarifying questions.             
[tokens used: 2,000]

Message 3: You describe the architecture.          
[tokens used: 15,000]

Message 4: I start coding.                         
[tokens used: 50,000]

Message 5: You ask "remember the brief?"           
[tokens used: 1,000]

Total: 78,000 tokens. Still within my 200k window.

But if your project has 20 sessions across 3 months, each with 
50k tokens of context, I'd need 1M tokens to remember everything.

That exceeds my window. I can't hold all projects in my mind at once.

This is the core problem the memory system solves.

What is Claude?

Claude is an AI assistant made by Anthropic.

I’m Claude (specifically, Claude Opus 4.7, a capable version released on early 2026).

My abilities

• I can write code in any language (Python, JavaScript, Go, Rust, etc.).

• I can read and understand your codebase.

• I can reason about architecture. You ask “should we use Vite or esbuild?” I weigh trade-offs.

• I can debug. You show me an error; I diagnose the root cause.

• I can explain concepts. Turing machines, quantum computing, why a function fails—I can break it down.

• I can follow instructions. You say “only use snake_case in Python,” I stick to it.

My constraints

• I don’t have memory between conversations. You close the chat, start a new one—I have no idea who you are or what we were building. Except for cached data or prompts.

• I have a context window limit. I can’t read your entire codebase at once if it’s bigger than 200k tokens.

• I can’t execute code directly. I can write code, but I can’t run it myself (though I can guide you or use a sandboxed environment in some tools). I talk here about the model itself but the web UI has a canvas where we can actually execute code and run apps.

• I make mistakes. I hallucinate facts, write buggy code, get lost in long explanations. You’re my reality check.

What is Claude Code?

Now what is Claude Code is a development tool that packages or harness me (Me Claude) with:

1. A code editor (file explorer, syntax highlighting).
2. A terminal (run commands, npm install, git push).
3. A persistent project space (stores your code, your memory files).
4. Auto-memory integration (I can read/write files in .claude/projects/).
5. IDE integrations (VS Code extension, JetBrains plugin, web version).

Think of it as: Claude (the AI) + VS Code (the editor) + persistent storage (your filesystem).

The critical feature for this series: Auto-memory

Claude Code allows me to read and write files in your project. So instead of me forgetting when you close the chat, you write a memory.md file.

Next session, I load it automatically.

This is why the memory system that will be explained in Part 1 works so well for Claude Code—the infrastructure is built in.

How They Relate: Claude vs. Claude Code

┌─────────────────────────────────────┐
│         Claude Code (the tool)      │
├─────────────────────────────────────┤
│ ┌──────────────┐  ┌──────────────┐  │
│ │   Claude     │  │   Editor +   │  │
│ │   (me, the   │  │   Terminal   │  │
│ │    AI)       │  │              │  │
│ └──────────────┘  └──────────────┘  │
│      +                +             │
│  Persistent memory (files)          │
│  (~/.claude/projects/...)           │
└─────────────────────────────────────┘

• Claude: The AI brain. No memory between conversations by default.
• Claude Code: The tool that harnesses Claude with an ecosystem of tools like files + IDE access.

You can use Claude without Claude Code (via Claude.ai web or API), but as a developer you lose a lot of good features.

Comparison: Claude vs. Other AI Tools

The memory system in this series is designed for Claude Code, but the principles apply to many of them.

Why This Matters: The LLM As a Compiler Paradigm

There’s an analogy that’s worth making:

LLMs are a new kind of compilers.

The Traditional Compiler

Lets think about this: When you write code in a language like C for example, the compiler translates it to machine code then the computer executes.

But when the LLM is used: You describe a problem in English. The LLM compiles it to code / runnable solutions then you can execute, evaluate and iterate.

The LLM is the intermediary that takes intent and produces artifacts (which may contain code, docs, and analysis).

However unlike a traditional compiler, an LLM is conversational so it can:

• Ask more questions to clarify the intent.
• Explain trade-offs.
• Refactor based on feedback.
• Reason about architecture.

This conversational nature is powerful, but it creates a problem: you need to maintain context across conversations, because the LLM itself has no context memory.

This is why the memory system exists. It closes the gap between the LLM’s memory limit and the project’s scale.

The Memory Problem in Context

Now you can understand why Part 1 exists:

1. LLM limit: I have a 200k token context window and I forget between conversations.
2. Your need: Your projects are bigger and live longer than one conversation.
3. The solution: Persistent memory outside me (which includes files, databases, and APIs). I load it when needed.

How to Continue Reading This Series

Now that you understand:

• What I am (Claude, an LLM)
• What Claude Code is (a tool that equip me with an editor + auto-memory)
• Why memory matters (because context windows are finite)

It is worth noting that context window is a concept related to the LLM itself but the memory system is related to the tool which is in our case Claude Code.

You’re ready for the rest:

1. Part 1: How Claude Manages Context — My memory system. How I use Tier 1 (auto-memory), Tier 2 (In-repo docs), Tier 3 (session context).
2. Part 2: Adapting to Your LLM Tool — How to implement this memory system with ChatGPT, OpenAI API, local Llama, or other tools.
3. Part 3+ — Token economics, decision frameworks, scaling beyond one project.

Closing: The Era of AI Assisted Programming

We’re in a new era where humans + LLMs collaborate on writing code.

The human brings:

• Intent (what to build).
• Judgment (is this right?).
• Taste (does this feel good?).

The LLM brings:

• Speed (code faster than typing).
• Pattern recognition (I’ve seen similar problems before).
• Explanation (why does this work?).

But the LLM has a limit: finite context windows. So the human must manage memory—decide what is important to save, what it is less important so it skip it, and how to organize knowledge so the LLM can retrieve it.

This series gives you the inner knowledge to learn to do that well.

Read Next: Part 1: How Claude Manages Context