C043 βš–οΈ Critical Reading 2 Prompts

Argument Map Prompt: Claims to Reasons to Evidence

Visualize argument structure: extract the main claim, supporting reasons, evidence chain, and unstated premises that hold it all together.

6 min read Structure Analysis Guide 3 of 5
PR007 The Argument Mapper
Use to map structure of persuasive text
Here’s a passage making an argument: “[paste passage]” Map the argument structure: – What’s the main claim? – What evidence or reasons support it? – What’s the logical chain connecting evidence to claim? – Are there any unstated premises I need to accept for this to work? – Draw a simple visual map if helpful.
PR020 The Assumption Hunter
Use to uncover hidden premises in arguments
Here’s an argument or claim: “[paste passage]” Find the hidden foundations: – What must I already believe for this argument to be persuasive? – What evidence is presented vs. assumed? – What alternative explanations does the author not consider? – What group of readers would find this convincing, and who wouldn’t? Why?
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Why Argument Maps Help You Read Better

Most arguments are invisible. Not because they’re hidden, but because prose obscures structure. A skilled writer weaves claims, evidence, and reasoning into flowing paragraphs that feel natural β€” so natural you might nod along without noticing what you’re actually agreeing to.

An argument map makes that invisible structure visible. It extracts the skeleton from the prose: the main claim sits at the top, the supporting reasons branch below it, and the evidence anchors each reason to something concrete. When you see arguments this way, you can evaluate them β€” not just experience them.

This matters because persuasion and validity are different things. A well-written argument can persuade you even when it’s logically weak. A poorly written argument can fail to persuade even when it’s sound. Mapping separates the writing from the reasoning.

How the Argument Mapper Prompt Works

PR007 (The Argument Mapper) asks AI to break down any argumentative passage into its structural components. You paste a passage, and AI returns four things: the main claim, the supporting evidence and reasons, the logical chain connecting them, and any unstated premises the argument requires you to accept.

What counts as a “main claim”? It’s the central assertion the passage wants you to accept. In an opinion piece about remote work, the main claim might be “Companies should adopt permanent hybrid policies.” Everything else in the passage exists to support that claim.

What counts as “evidence”? Evidence is the concrete stuff: statistics, studies, examples, expert quotes, historical precedents. Reasons are the interpretive layer that explains why evidence supports the claim.

Example Argument Map

Main Claim: Cities should dramatically increase public transit investment.

Supporting Reasons:
1. Transit reduces traffic congestion β†’ supported by Denver study showing 23% reduction
2. Transit is more environmentally sustainable β†’ supported by carbon emissions comparison
3. Transit increases economic mobility β†’ supported by job access statistics

Unstated Premises: Assumes cities have budget capacity; assumes transit will be used if built; assumes benefits outweigh costs.

Going Deeper with the Assumption Hunter

PR007 reveals explicit structure. PR020 (The Assumption Hunter) digs into implicit foundations β€” the beliefs you must already hold for the argument to work.

Every argument has hidden premises. “We should invest in transit because it reduces congestion” assumes you believe congestion is a problem worth solving. “Transit increases economic mobility” assumes you value economic mobility. Making these explicit helps you understand why people disagree.

Someone who believes cities shouldn’t subsidize transportation will find the transit argument unpersuasive β€” not because the evidence is wrong, but because they don’t share the underlying values. The Assumption Hunter identifies these fault lines.

πŸ’‘ Pro Tip

After mapping an argument, ask: “What would need to be true for this argument to fail?” This question often reveals assumptions faster than analyzing them directly. Work backward from failure to find hidden requirements.

When to Use Argument Mapping

Opinion pieces and editorials: These are explicitly persuasive. Mapping reveals what they’re actually asking you to accept.

Policy proposals: Government documents, think tank reports, and advocacy pieces all make arguments. Mapping shows the structure beneath the prose.

Academic arguments: Research papers have argument structures too β€” hypotheses, evidence, conclusions. Mapping helps you see whether conclusions follow from data.

Persuasive business writing: Marketing copy, pitch decks, strategic memos. Once you see argument structures, you can’t unsee them.

Continue to the What’s Missing Prompt (C044) for gap analysis, or the Assumption Hunter Guide (C045) for deeper assumption work. Explore all tools in the Critical Reading pillar.

Frequently Asked Questions

Evidence is concrete: statistics, studies, examples, quotes. Reasons are the interpretive layer explaining why evidence supports the claim. “Productivity increased 15% during remote work” is evidence. “Remote work increases productivity” is a reason derived from that evidence.
Ask: what does the author want me to believe or do by the end? Everything else exists to support that conclusion. In persuasive writing, the main claim is often stated in the introduction or conclusion. In subtle writing, it may be implied rather than explicit.
PR007 (Argument Mapper) first β€” understand the explicit structure before digging into implicit foundations. PR020 (Assumption Hunter) second β€” once you see the structure, examine what holds it together. Mapping, then hunting.
Argument mapping works best on persuasive text. Informational or narrative text may not have argument structures to map. If AI returns a weak or forced map, the passage probably isn’t argumentative. Use different prompts β€” summarization or analysis instead.
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