Accuracy Check: Verify the Summary Against the Text

The Problem with Blind Summaries

AI summaries are convenient but unreliable. Large language models can hallucinate details that weren’t in the source, conflate similar concepts, drop crucial qualifications, or misrepresent the author’s actual emphasis. The summary sounds plausible — that’s what makes it dangerous.

You can’t catch these errors by reading the summary alone. The only way to verify an AI summary is to check it against the original text. These two prompts make that verification systematic rather than hoping you’ll notice problems.

The base summary prompt (C015) gets you a good first draft. These verification prompts ensure that draft is actually accurate.

Two-Step Verification

Step 1: PR035 — Check your understanding mid-process. Before you finalize anything, state what you think the text means and how confident you are. AI compares your understanding against the source and tells you whether you’re on track. This catches misinterpretations before they become embedded in your notes.

The key is the confidence rating. If you say “high confidence” and you’re wrong, that’s a different problem than “low confidence” and you’re wrong. The prompt adjusts its response accordingly — high confidence errors get correction, low confidence gets clarification.

Step 2: PR033 — Explain it back for evaluation. After reading, explain the text in your own words. AI evaluates your explanation against the source and identifies four things: what you got right, what you got wrong or oversimplified, what you missed entirely, and what you understand superficially versus deeply.

This is the Feynman technique with AI as your checker. The act of explaining exposes gaps in your understanding that passive reading hides.

What Verification Catches

Hallucinated details: AI sometimes adds specifics that weren’t in the source — statistics, names, dates, or claims that sound right but aren’t there. Verification prompts ask AI to point to where each claim appears in the original.

Conflated concepts: When an article discusses two related but different ideas, AI might blur them together. Verification catches when your understanding merges things that should stay separate.

Dropped qualifications: The original says “in some cases” or “under certain conditions.” The summary says “always.” These subtle shifts can completely change meaning. Verification flags where nuance got lost.

Misplaced emphasis: The author’s main point might become a minor detail in your summary, while a supporting example becomes the headline. Verification helps you see whether you’ve captured the actual argument structure.

When to Use Verification

You don’t need to verify every summary. Save verification for content that matters:

Decision-relevant summaries: If you’ll act on the information — making recommendations, changing strategy, advising others — verification prevents costly errors.

Shared summaries: If you’ll send the summary to colleagues, publish it, or use it in presentations, verification protects your credibility.

Learning material: If you’re studying for retention, verification prevents you from learning incorrect information that’s harder to unlearn later.

For casual browsing and personal curiosity, verification is overkill. Use it when accuracy has consequences.

Limitations of AI Verification

AI checking AI isn’t perfect. The same model that made errors can miss them when reviewing. Think of verification as adding another check, not guaranteeing accuracy.

For high-stakes content — medical information, legal claims, financial decisions — human verification remains essential. Use AI verification to catch obvious errors and flag areas for human review, not as the final word.

Return to the Summarize Articles pillar for more summary formats, or explore the full AI for Reading hub.