Claude by Anthropic consistently ranks as the most capable AI for reasoning, writing, and coding. Its 200,000-token context window — the largest available — lets it analyze entire books, full codebases, and lengthy legal documents in a single conversation.
The longest context of any mainstream AI. Paste an entire 300-page book, a full codebase, or a lengthy contract and ask questions about all of it. Other leading models max out at 32k–128k tokens.
Claude consistently wins coding benchmarks. It writes readable, well-commented code with proper error handling, follows language best practices, and explains architectural decisions. Particularly strong in Python, JavaScript, TypeScript, and Rust.
Drag and drop any PDF, Word doc, spreadsheet, or image into the chat. Claude extracts key information, answers precise questions, compares documents, and identifies patterns across large amounts of text.
Produces the most natural-sounding, consistent long-form content — technical documentation, articles, scripts, and reports. Maintains consistent voice and avoids the repetitive patterns common in other AI writing tools.
Create named Projects with system-level instructions. Claude remembers your context, writing style, and preferences across all conversations in a project — essential for ongoing work like writing a book or maintaining a codebase.
Trained by Anthropic with Constitutional AI to be genuinely helpful, harmless, and honest. Claude is more likely to say it is uncertain than to confidently hallucinate. It will push back on factually incorrect premises.
Claude Projects are dedicated workspaces with custom instructions, uploaded knowledge files, and conversation history. Create separate Projects for: Software Development (with codebase conventions), Content Marketing (with brand voice guide), Client Work (with client-specific context). Every new conversation starts with Claude already knowing your context — no re-explaining. Projects are available on all plans; Free plans can create up to 5 Projects.
Skills are ZIP packages containing a SKILL.md file that teaches Claude a specialized, repeatable task. A Brand Guidelines Skill applies your company's colors, fonts, and tone to every document. A Code Review Skill enforces your team's specific standards. Skills work in Claude.ai, Claude Code, and the API — build once, use everywhere. Available on Free, Pro, Max, Team, and Enterprise plans.
Upload up to 200MB of documents per Project: PDFs, Word docs, code files, style guides, research papers, SOPs. Claude reads and synthesizes across all uploaded files simultaneously. Ask 'What are the main findings across all these research reports?' and Claude answers from your specific documents. Supported formats: PDF, .txt, .md, .html, .docx, .csv, .py, .js, and most code file types.
Go to claude.ai → Sign up with Google or email. Free plan: Claude Sonnet with daily message limits. Pro ($20/mo): Claude Opus 4.5, 5x more usage, Projects with file storage, and Priority access.
The interface: left sidebar has conversation history and Projects. Main area is the chat. Paperclip icon uploads files. The model name shown top-center tells you which model is active.
First test: paste any 2-3 paragraph document and ask Summarize this in 3 bullet points and identify the single most important claim. Notice how Claude reads the full text, not just skims it.
This is Claude's strongest use case. Download any real document — a company annual report (10-K), a research paper, or a contract. Upload it via the paperclip icon.
Use this analysis prompt:
I have uploaded [Document Name]. Analyze it and give me: 1) A 3-sentence executive summary anyone can understand. 2) The 3 most important findings or decisions. 3) Anything that seems risky, unusual, or worth investigating further — be specific. 4) The 3 questions this document raises but does not answer. Use specific page or section references.
Claude reads the entire 100+ page document — including footnotes — and synthesizes it in under 60 seconds. This alone replaces hours of manual reading for analysts, lawyers, and researchers.
Click Projects → New Project. Name it clearly (e.g., Work — Legal, Client A, Coding — Python).
Write your instructions in the text field. Use this structure:
About me: I am a [role] at [company type]. Context: [describe what you do]. My preferences: [output format, writing style, level of detail]. Always: [specific rules Claude must follow]. Never: [what Claude must avoid]. When I ask for [task type], always include [specific thing].
Then upload your knowledge files: click Add content → upload PDFs, code files, style guides, research papers, or documents. Every conversation in this Project uses these files as context. Claude can answer questions about any uploaded content across all files simultaneously.
Paste code and use this specific prompt structure to get professional results:
Review this [language] code as a senior engineer focused on correctness, performance, and maintainability. For each issue: 1) Category: [BUG / SECURITY / PERFORMANCE / READABILITY]. 2) Explain the problem and why it matters. 3) Show the fixed version side-by-side. End with an overall rating 1-10 and explanation.
For generating new code:
Write a [language] function that [exact description]. Requirements: [list]. Edge cases to handle: [list]. Include: type annotations, JSDoc/docstring, error handling for [specific failures], and 3 unit test examples. Use [library/pattern] over alternatives.
This is Claude's most unique capability. Upload 4-8 documents on the same topic and ask questions that would take hours to answer manually.
Example: upload 6 competitor pricing pages, then ask:
Across all these competitor documents: 1) What pricing models are they using (per-seat, usage-based, flat)? 2) What features does every competitor include in their base plan? 3) What features do they charge extra for? 4) What are they NOT offering that might be a market gap? 5) How does each position their value proposition? Create a comparison table for the first 3 points.
Claude synthesizes across all documents simultaneously. No copy-pasting between chats, no manual cross-referencing.
Create a file named SKILL.md in a new folder:
--- name: Code Review description: Structured code review following our standards. Invoke when asked to review code. --- ## Review Process 1. Bugs: logic errors, null pointer risks, race conditions 2. Security: injection, auth gaps, data exposure 3. Performance: N+1 queries, inefficient loops, blocking calls 4. Readability: unclear naming, missing types, no docstrings ## Output Format For each issue: [SEVERITY: Critical/High/Medium/Low] description fix End: Overall score /10 and the single most important change.Compress the folder to ZIP. Go to Claude.ai → Settings → Customize → Skills → Create skill → Upload ZIP → Enable toggle. Claude now applies these standards automatically whenever you share code.
Install the SDK:
npm install @anthropic-ai/sdk # Node.js pip install anthropic # PythonGet your API key at console.anthropic.com → API Keys → Create Key. Store it as an environment variable.
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic();
// ANTHROPIC_API_KEY env variable auto-detected
const response = await client.messages.create({
model: 'claude-opus-4-6',
max_tokens: 1024,
system: 'You are a helpful assistant.',
messages: [{ role: 'user', content: 'Hello!' }]
});
console.log(response.content[0].text);The free API tier gives you enough credits to build and test. Upgrade to a paid tier for production workloads.Claude produces the most factually consistent long-form content of any AI. Use this workflow for a 2,000-word article:
Step 1 — Brief Claude:
I am writing a 2,000-word article for [audience] about [topic]. Core argument: [one sentence]. The main question the reader wants answered: [question]. Tone: [tone]. Before writing, ask me any questions that would make the article significantly better.
Step 2 — Approve the outline before writing begins.
Step 3 — Section by section:
Write section 2 from the approved outline. Include [specific example]. End with a transition to section 3. Show your reasoning.
Section-by-section writing consistently produces better output than asking for the full article at once.