# Learn Claude — Part 4: Files, Vision & Real Data Analysis

Typed prompts only scratch the surface. Claude's practical power multiplies when you feed it **your material** — documents, screenshots, spreadsheets — and this is where the hallucination problem from Part 1 gets its most important fix.

## Grounding: why files change everything

When Claude answers from training data, it's recalling — and recall can be wrong. When Claude answers **from a document in its context window**, it's reading. Answers become checkable against a source you both share. Professionals exploit this constantly:

> Instead of *"What does GDPR say about data retention?"* (recall, hallucination-prone) upload the actual regulation and ask *"According to this document, what are the data-retention rules? Quote the relevant sections."* (reading, verifiable)

The instruction **"answer only from the document, and say if it's not covered"** is one of the most valuable sentences in this series.

## Documents: the core workflows

Drag any file into the chat — PDFs, Word docs, text, code. The moves that matter:

*   **Targeted extraction:** "List every deadline, penalty, and obligation in this contract as a table."
    
*   **Guided summary:** "Summarize this 60-page report *for someone deciding whether to fund the project*." A summary's audience changes what belongs in it — always state one.
    
*   **Interrogation:** "What does this lease say about early termination? Quote it." Follow-ups are free; the file stays in context.
    
*   **Cross-document work:** upload two versions and ask "What changed between these, and which changes shift risk to me?" Multi-file comparison is something humans are slow at and Claude is fast at.
    
*   **Transformation:** meeting notes → action-item email; research paper → blog outline; requirements doc → test checklist.
    

## Vision: Claude can genuinely see

Uploaded images aren't decorations — Claude analyzes them:

*   **Screenshot of an error** → "Diagnose and fix." (Often faster than copying the text.)
    
*   **Whiteboard photo** → "Turn this into structured notes with action items."
    
*   **A chart** → "What's the trend, and what's misleading about how this is presented?"
    
*   **UI screenshot** → "Critique this design for usability."
    
*   **Handwriting, foreign-language forms** → transcribe, translate, explain each field.
    

One built-in boundary: Claude won't identify real people in photos.

## The professional layer: executed data analysis

Here's the distinction that separates casual use from real analysis. From Part 1: Claude's mental arithmetic is unreliable. The fix is built in — the **analysis tool / code execution** (enable it in Settings if needed). When it's relevant, Claude **writes actual code, runs it on your file, and reports the computed results.**

Upload a CSV and try:

*   "Compute monthly revenue growth and flag anomalies. Use code, show your work."
    
*   "Clean this data: trim whitespace, standardize dates, list duplicates — then give me the cleaned file."
    
*   "Which two columns correlate most strongly? Plot it."
    

The output is the difference between *"revenue seems to grow around 10%"* (estimated, maybe hallucinated) and *"revenue grew 11.4% month-over-month"* (computed). When numbers matter, the magic phrase is **"use code to calculate this."**

## Knowing the limits

1.  **Size ceilings exist.** Very large files can exceed the context window. Strategies: split by chapter, or summarize sections in separate chats and synthesize the summaries (context distillation again, from Part 2).
    
2.  **Long-document attention.** In huge documents, details buried mid-file get less attention than the start and end. For contracts and compliance work, ask section-targeted questions rather than one giant "check everything."
    
3.  **Scanned PDFs vary.** Claude handles them, but low-quality scans degrade extraction — spot-check quotes against the page.
    
4.  **Verification discipline stays.** Grounding shrinks hallucination; it doesn't abolish it. For consequential documents, verify quoted passages exist. The habit costs a minute.
    

## A realistic workflow: competitor research

1.  Upload three competitor pricing PDFs → "Extract all plans and prices into one comparison table."
    
2.  "Use code: compute the average price per feature tier."
    
3.  "Which competitor changed positioning compared to this older PDF?" (upload it)
    
4.  End with distillation: "Summarize the strategic picture in 10 bullets" → carry that into a fresh chat for strategy work.
    

Four prompts. That used to be an afternoon.

## Exercise

Take a real spreadsheet or export (bank statement, sales data, anything). Ask one question *without* code, then the same question *with* "use code to calculate this." Compare the answers — this lesson sticks best when you see the difference yourself.

## Next up

**Part 5:** Artifacts — turning conversations into working documents, tools, and apps you can actually ship.