Claude was built by Anthropic — founded in 2021 by Dario Amodei, Daniela Amodei, and a team of ex-OpenAI researchers. Their goal was never just to build a powerful AI. It was to build one that was safe, honest, and genuinely beneficial.

That founding principle shapes everything Claude does. Unlike companies that treat safety as a compliance checkbox, Anthropic's entire business model is built around the idea that advancing AI safely is itself the product.

This is why Claude behaves differently from other AI models you may have used. It acknowledges uncertainty, reasons from principles, and will often explain why it responds a certain way — rather than just producing an output.

Three things set Claude apart from the field: its approach to safety, its genuinely long context window, and the way it handles nuance and ambiguity. A fourth — exceptional writing quality — makes it the professional's choice for content work.

Anthropic trains Claude with a written "constitution" — a set of principles that guide how it responds. Not just rules, but values baked into reasoning. This is why Claude can explain its thinking, not just execute a task.

Claude offers four tiers: Haiku for speed, Sonnet for balance, Opus for depth, and Fable for creative writing. Match the model to the task — and cut costs or boost quality as needed.

The question isn't "can Claude do X?" — it's "how do I use Claude to do X at a level I couldn't reach alone?" The professionals extracting the most value aren't just speeding up tasks. They're removing entire categories of work from their plates.

Claude is what's called a Large Language Model (LLM) — a type of AI trained to predict, generate, and reason with language. It doesn't "look things up" in a database. It compresses patterns from billions of examples into a statistical model of how language — and ideas — work.

Think of it like this: Claude read the internet, millions of books, research papers, and code repositories — not to store facts like a hard drive, but to learn the deep patterns underlying human knowledge. When you ask it a question, it's not searching. It's reasoning.

Beneath Claude's intelligence is a transformer neural network — the breakthrough architecture behind every major LLM since 2017. Transformers learn relationships between words, concepts, and ideas across vast distances in a text. That's why Claude can summarise a 100-page report with accurate nuance, or continue your half-written sentence in your exact voice.

This is also why Claude sometimes hallucinates — confidently producing plausible-sounding but incorrect information. The model is pattern-matching, not fact-checking. Understanding this shapes how you use it: always verify high-stakes factual claims from original sources.

Most AI safety approaches are bolt-ons: a content filter checks outputs before they reach you. Constitutional AI works differently. Anthropic trained Claude with a written "constitution" — a document of principles covering honesty, harm avoidance, and helpfulness. Claude was trained to reason against this constitution, not just check boxes.

Claude doesn't read text the way you do. It processes tokens — chunks of characters, not words. "fantastic" might be one token. "un-be-liev-able" might be four. Punctuation, spaces, and code syntax each consume tokens too. Understanding this changes how you structure long prompts.

The context window is Claude's working memory — the total number of tokens it can hold in mind at once. Claude's current context window extends up to 200,000 tokens — roughly 150,000 words, or an entire novel. Everything you've written in the current conversation lives inside this window.

When you start a new conversation, Claude's context resets completely. It has no memory of previous chats. This is why power users save key context, build system prompts, and use Projects — to give Claude persistent working memory.

Claude processes exactly what you give it. If you give it vague input, you get vague output. If you give it structured, contextual input — you get structured, contextual output. This isn't a flaw in the model. It's the core mechanic of how LLMs work.

Most bad prompts fail for one of three reasons. Understanding these gaps is the first step to eliminating them permanently.

The fix is a framework — a reliable structure that ensures every prompt covers the inputs Claude needs to produce professional output. That framework is RTCF, and it's the backbone of this lesson.

Every high-performing Claude prompt contains four elements: a Role that defines Claude's persona, a Task that specifies the exact deliverable, Context that provides the situation and constraints, and a Format that defines how the output should look. Miss any one — quality drops.

This prompt takes 30 seconds to write and produces a result you could ship. The untrained version — "write me a headline for my scheduling app" — produces something generic that needs five rounds of editing.

Claude's 200K token context window is enormous — but it's finite. Professional users treat the first message as a system brief that shapes every response that follows.

Claude's Projects feature lets you set a persistent system prompt that loads at the start of every conversation. For recurring work — content creation, code review, client communication — this eliminates the opening brief entirely. Define it once, use it forever.