Day 2 of 5
⏱ ~60 minutes
How to Use Claude — Day 2

Claude for Documents: Analyze Anything

Claude's 200K token context window is its biggest competitive advantage. Today you'll use it to do in minutes what used to take hours — summarizing long reports, extracting data from contracts, and pulling structured information from research papers.

Why the Context Window Changes Everything

Before AI assistants, "reading" a 100-page document meant either reading the whole thing or spending 20 minutes skimming and hoping you didn't miss anything important.

Claude's context window — 200,000 tokens, or roughly 150,000 words — means you can paste an entire long document and ask Claude questions about the whole thing at once. Not a summary. Not a chunk. The whole thing.

Context window comparison

Claude 3.5 Sonnet — 200K tokens (~150K words / ~300 pages)
200K
GPT-4o — 128K tokens (~96K words / ~190 pages)
128K
Gemini 1.5 Pro — 1M tokens (yes, but quality degrades on long docs)
1M
💡
Quality matters more than size. Gemini has a 1M token window but consistently performs worse on complex analysis of long documents. Claude at 200K produces better, more accurate analysis. Don't just count tokens.

Uploading PDFs and Documents

On claude.ai, you can upload PDFs directly using the paperclip icon in the chat. Claude will read the full document — not just the first few pages, not a preview. The whole thing.

If you don't have a PDF handy, you can also paste the full text of a document directly into the chat. For very long documents, paste first and say "I'm pasting a document — hold for instructions" before giving your prompt.

ℹ️
File types Claude can read: PDF, TXT, CSV, HTML, Markdown, code files (.py, .js, .ts, etc.), Word documents (.docx on paid plans). Images (Claude can read text in images too). What it can't do: Excel files with formulas — convert to CSV first.

Summarizing Long Reports

The most common use case. You have a 50-page report. You need the key points in 5 minutes.

Prompt — Report Summary
[Paste or upload your document]

You are a senior analyst summarizing this report for
a busy executive who has 3 minutes to read your summary.

Task: Summarize this report with the following structure:
1. One-sentence bottom line (what is the core finding?)
2. 5 key findings, each in 1-2 sentences
3. Any data or statistics that support the most
   important finding
4. What action this report recommends (if any)

Be direct. If the report is unclear or contradicts
itself, flag that explicitly.

Notice what this prompt does: it gives Claude an audience (busy executive), a structure (4 numbered sections), and a constraint (flag contradictions). This is how you get useful summaries instead of a 10-paragraph wall of text that's just the document again but shorter.

Extracting Data from Contracts

Contracts are perfect for Claude. They're long, full of legal language, and you almost always need to pull specific information out of them.

Prompt — Contract Data Extraction
[Upload or paste contract]

Extract the following information from this contract
and format it as a JSON object:

{
  "parties": ["party 1 name", "party 2 name"],
  "effective_date": "YYYY-MM-DD",
  "term_months": number,
  "payment_amount": "dollar amount",
  "payment_schedule": "description",
  "termination_notice_days": number,
  "key_obligations_party1": ["list of 3-5"],
  "key_obligations_party2": ["list of 3-5"],
  "automatic_renewal": true/false,
  "governing_law": "state/jurisdiction",
  "unusual_clauses": ["anything non-standard"]
}

If any field is not present or unclear, use null
and note why in a separate "notes" field.

This prompt asks for structured JSON output, which means you can paste Claude's response directly into a database, spreadsheet, or another tool. You can change the format to a table, a list, or anything else that fits your workflow.

Asking follow-up questions

After Claude reads a document, you can keep asking questions in the same conversation:

Follow-up Prompts
# After the initial extraction:

"Does this contract include any non-compete or
non-solicitation clauses? Quote the exact language."

"Compare section 4.2 to the standard terms — is
anything unusual about the liability cap?"

"What would happen if party A misses a payment?
Walk me through the exact sequence of remedies."

"Summarize any clauses I should flag for a lawyer
before signing."

Extracting Data from Research Papers

Research papers have a specific structure: abstract, methodology, results, discussion. You can use that structure to get very precise extractions.

Prompt — Research Paper Extraction
[Upload or paste research paper]

Extract the following from this research paper:

**Study Overview**
- Research question (1 sentence)
- Hypothesis (if stated)
- Study design (RCT, observational, meta-analysis, etc.)
- Sample size and population

**Methodology**
- Key variables measured
- Statistical methods used
- Any significant limitations the authors acknowledge

**Results**
- Primary finding with effect size or p-value
- Secondary findings (top 3)
- Any null results

**My Takeaway**
- Is this finding generalizable? Why or why not?
- What would this mean for someone in [my industry]?
- Should I trust this study? (check methodology quality)

Replace [my industry] with the relevant context.
⚠️
Important: Claude can misread statistics or misinterpret methodology, especially in highly technical papers. Always verify key numbers by checking the original text. Use Claude to speed up your reading, not to replace it entirely on high-stakes decisions.

Comparing Multiple Documents

Claude can compare two documents in a single conversation. Upload or paste both, label them clearly, and ask for a comparison.

Prompt — Document Comparison
I'm going to give you two documents. Please read both
before responding.

DOCUMENT A:
[paste or upload first document]

DOCUMENT B:
[paste or upload second document]

Now compare them:
1. What are the 3 most significant differences?
2. Where do they contradict each other?
3. Which document is more favorable to the buyer?
4. If I had to choose one, which would you recommend
   and why?
📝 Day 2 Exercise
Extract Structured Data from a Real Document

Find a real document from your work — a contract, a report, a research paper, even a long email thread. You're going to extract structured data from it.

  1. Upload or paste the document into a new Claude conversation.
  2. Write a prompt that asks Claude to extract at least 5 specific pieces of structured information. Define the output format (JSON, table, bullet list — your choice).
  3. Ask at least one follow-up question based on Claude's response (dig into something specific).
  4. Check Claude's extraction against the original document. Find at least one thing to correct or clarify.
  5. Ask Claude: "What information in this document did you find ambiguous or unclear?" This reveals what humans also find confusing about the doc.
💡
The verification habit: Every time Claude extracts a number or a specific claim from a document, spot-check it against the original. Not because Claude is usually wrong — it's usually right — but because the times it's wrong are the times it will cost you. Build this habit now.

Day 2 Summary

  • Claude's 200K context window means you can analyze entire contracts, reports, and research papers in one conversation.
  • Specify your output format (JSON, table, bullet points) to get data you can actually use downstream.
  • Keep asking follow-up questions in the same conversation — Claude remembers the full document.
  • Always verify extracted numbers and key claims against the original. Claude is a fast first-pass, not a final authority.
Challenge

Find a document you've been meaning to read but haven't — a report, white paper, or research paper that's been sitting in your downloads folder. Give it to Claude and ask it to give you the "if I only had 5 minutes" version. Then decide if the full document is worth your time. That's the actual power move here.