Level 2 · Guide 06
Analyze files with traceable evidence
When Claude analyzes a file, require it to separate extracted facts, interpretation, and items it cannot verify. Sample-check important numbers and quotations against the original page, table, or cell before using the result.
Product features may change. Check the last verified date and official sources.
Core concepts to know first
Focus on the decisions these terms support in real work rather than memorizing them.
Core concepts
1. Build a file inventory
A useful file summary preserves provenance. Record where each fact came from, identify interpretation as interpretation, and expose unreadable or missing sections instead of allowing the model to bridge them silently. In this guide, the first observable move is to define the questions and required evidence location format.
2. Preserve evidence pointers
Extract facts, interpretations, and unknowns into separate columns. Preserve the approved input and the evidence behind the result so a reviewer can distinguish what the source says from what Claude inferred. This directly controls the risk that scanned pages may be read incorrectly.
3. Separate direct evidence from interpretation
Sample-check high-impact numbers and quotes in the source file. Record the decision and the remaining uncertainty instead of hiding it in polished prose. The review must explicitly test whether a summary can lose footnotes and exceptions.
4. Separate extraction from judgment
Treat the possibility that spreadsheet totals may hide formula or filter errors as a required test case. The intended result is a file analysis table with traceable evidence locations, not an unreviewed answer that merely looks complete.
Synthetic work scenario
How this applies at work
This scenario was written for learning and is not a real customer case.
A fictional finance team reviews a synthetic quarterly PDF and spreadsheet. It asks for page and cell references, samples totals against the originals, and flags one chart label that cannot be read confidently.
The scenario is newly written for this guide and is neither a customer case nor a performance claim. Its specific deliverable is a file analysis table with traceable evidence locations. A reviewer can reproduce the work from the synthetic inputs without access to customer, employee, health, contract, or confidential company data.
Try it yourself
Choose one small task and follow the steps. Confirm organizational policy and data boundaries before using sensitive materials.
Step 1. Define the questions and required evidence location format
Define the questions and required evidence location format. Use only approved synthetic material and record both the evidence and any remaining uncertainty.
Verify: Confirm that another reviewer can reproduce the input, result, evidence, and stop point.
Step 2. Extract facts, interpretations, and unknowns into separate columns
Extract facts, interpretations, and unknowns into separate columns. Use only approved synthetic material and record both the evidence and any remaining uncertainty.
Verify: Confirm that another reviewer can reproduce the input, result, evidence, and stop point.
Step 3. Sample-check high-impact numbers and quotes in the source file
Sample-check high-impact numbers and quotes in the source file. Use only approved synthetic material and record both the evidence and any remaining uncertainty.
Verify: Confirm that another reviewer can reproduce the input, result, evidence, and stop point.
Completion checklist
Check only what you verified yourself. Every item must be checked before saving completion.
Some items are still unchecked. Review the result again.
What could go wrong?
Plausible language does not guarantee accuracy. Compare the result with originals, calculations, permissions, and current information.
- Scanned pages may be read incorrectly
- A summary can lose footnotes and exceptions
- Spreadsheet totals may hide formula or filter errors
Boundaries that require human review
Academy practice stops at draft, preview, or approval pending. Actions with real impact require separate owner approval outside Academy.
What AI can do
- Step 1. Define the questions and required evidence location format
- Step 2. Extract facts, interpretations, and unknowns into separate columns
- Step 3. Sample-check high-impact numbers and quotes in the source file
What a person must approve
- Applicable law, contract, organizational security policy, and explicit approval boundaries take priority. Stop and ask the responsible owner when they conflict.
- Academy exercises never send, publish, purchase, delete, execute contracts, or change permissions. A responsible person performs any real action through a separate process.
This guide is educational and does not replace legal, security, or privacy judgment for your organization.
Questions about this guide
- When is this guide complete?
- It is complete when the stated outcome is ready and another reviewer can retrace the inputs, evidence, boundaries, and decision. The target outcome is “A file analysis table with traceable evidence locations.”
- May I practice with real company data?
- No. Use synthetic material in the Academy. Real data requires a separate review of policy, legal basis, contracts, minimization, retention, deletion, and the approved environment.
- What if the product screen differs from this guide?
- Product behavior can change. Check the verification date and official sources, then retest the current account and plan with a small, low-risk example.
Official sources and further reading
Recheck the current product and policy status in these primary sources.
Authorship and review
Save progress
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