Learn Claude — Part 1: What Claude Is & How It Actually Works
Welcome to Learn Claude, a series that takes you from your first message to professional-level workflows: advanced prompting, data analysis, building apps, Claude Code, and the API. This first post builds the mental model everything else depends on. Skip it, and the later parts will feel like magic tricks. Read it, and they'll feel obvious.
What is Claude?
Claude is an AI assistant built by Anthropic, an AI safety company. On the surface, it's a chat interface: you write, it answers. Underneath, it's a large language model (LLM) — a neural network trained on enormous amounts of text to predict what comes next in a sequence.
That "predict the next word" framing sounds trivial, but at sufficient scale it produces something remarkable: a system that can reason through problems, write working code, analyze documents, and explain almost anything. You don't need the math — but three concepts will make you dramatically better at using it.
Concept 1: Tokens — how Claude reads
Claude doesn't see letters or words. Text is broken into tokens — chunks of roughly 3–4 English characters. "Understanding" might be two tokens; "cat" is one.
Why should you care?
Limits are measured in tokens. Message limits, document sizes, response lengths — all token-based.
Cost (on the API) is per token. Verbose prompts literally cost more.
It explains quirks. Counting letters in a word is oddly hard for LLMs because they never see individual letters.
Concept 2: The context window — Claude's working memory
Everything Claude can "see" during a conversation — your messages, its replies, uploaded files, instructions — lives in the context window. Claude's is huge (hundreds of pages of text), which is one of its signature strengths.
Two professional-grade implications:
Claude re-reads the entire conversation on every message. Nothing is "remembered" between turns in some hidden brain — the memory is the transcript. This is why long chats slow down and consume usage limits faster.
When context fills up, quality degrades before it fails. Details from early in a very long conversation get less attention. Pros start fresh chats deliberately and re-supply only the relevant context.
We'll turn this into concrete conversation-management tactics in Part 2.
Concept 3: Training cutoff vs. tools
Claude's base knowledge stops at its training cutoff date. It doesn't inherently know today's news, prices, or your company's data. What closes the gap is tools: web search, file uploads, code execution, and connectors to apps like Google Drive. A large part of professional Claude usage is knowing which knowledge lives in the model and which must be brought in via tools. This series covers all of them.
The model family
Claude ships in several models — a speed/capability trade-off:
Haiku — fastest and cheapest; ideal for simple, high-volume tasks
Sonnet — the balanced workhorse for everyday work
Opus and above — maximum capability for complex reasoning, hard coding problems, and long documents
You can switch models mid-conversation from a dropdown. A practical pattern you'll use later: draft and explore with a fast model, then switch up for the hard final step.
What Claude is genuinely good and bad at
Strong: writing and editing, coding, summarizing and analyzing long documents, structured reasoning, translation with natural phrasing, brainstorming, explaining at any level.
Weak — know these cold:
Hallucination. Claude can state false things fluently and confidently — especially niche facts, statistics, citations, and URLs. Verification is your job; we'll build it into every workflow in this series.
Arithmetic at scale. For real calculations, have Claude write and run code (Part 4) instead of computing "in its head."
Real-time anything — without web search enabled.
The one-sentence summary
Claude is a reasoning engine with a large but finite working memory, frozen knowledge, and pluggable tools — and your skill is in what you put into that memory.
Every technique in the next seven parts is an application of that sentence.
Exercise
Ask Claude: "Explain how large language models work, then explain what tokens and context windows are, using an analogy from cooking." Then ask: "What are your own limitations I should watch out for?" — Claude is refreshingly honest about itself.
Next up
Part 2: setting up properly and mastering conversation mechanics — model switching, context hygiene, and the settings most users never find.
