Paste three articles into a chatbot and ask for a summary, and here’s what you get: three summaries stapled together. That’s not synthesis, just shorter versions of the same sources sitting in a pile, with no sense of where they actually clash. A Redditor in r/PromptEngineering, u/Prior-Philosophy7352, ran into exactly this wall and built a prompt that fixes it.
Here’s the short version: instead of asking the model to summarize each source, you ask it to compare them. Find the agreements, isolate the disagreements, flag the claims nobody else backs up, then write one real conclusion. That reframe changes everything about the output.
Why “Summarize These” Falls Flat
When you hand a model several sources and ask for a summary, it defaults to the easiest path through the task. It processes each input on its own, then hands back a shortened version of each one in order. Nothing forces it to look across the sources at all.
The original poster put it well. Getting each source summarized in turn is “just shorter versions stacked in a pile.” Nothing points out where two sources contradict each other, and nothing tells you which claims are actually solid because multiple sources back them up.
The Prompt That Forces Comparison
Here’s the exact prompt, reproduced word for word:
I’m giving you several sources on the same topic. Do not summarize them one by one.
Instead:
- State the 3 to 5 points where the sources agree.
- State every point where they disagree, and note which source says what.
- Flag any claim that appears in only one source and isn’t backed elsewhere.
- Write a 4 to 6 sentence synthesis of what a careful reader should conclude, including where the picture is still unsettled.
Rules:
- Attribute claims to sources by name or number. No blending everything into one voice.
- Don’t import outside knowledge. Work only from what I paste.
Sources:
[paste source 1]
—
[paste source 2]
—
[paste source 3]
Copy it, drop your sources underneath, and run it.
Why It Works
Two lines are doing all the heavy lifting.
The first is “do not summarize them one by one.” That single instruction blocks the model’s default move. Without it, most models fall back to a list of mini-summaries because that’s the safest, laziest path through the task.
The second is the attribution rule: claims get tied to a source by name or number. This is what keeps the model honest. Once a model has to write “Source 2 claims X, but Source 1 says Y,” it can’t blend two conflicting claims together. It can’t smooth them into one noncommittal sentence. It has to show its work, so the disagreements survive instead of getting sanded down.
The instruction to flag claims backed by only one source is the quiet MVP here. Anything unique to a single source and repeated nowhere else is usually the exact thing you’d otherwise repeat without checking. Having the model surface it automatically saves you a fact-check pass later.
Use Cases 🎯
This prompt earns its keep anywhere you’re pulling from more than one source and need a real read on the situation, not a recap:
- Comparing analyst notes or earnings call takeaways before a trade
- Reviewing competing product reviews before you buy
- Cross-checking news coverage of the same story from different outlets
- Reconciling conflicting documentation from different teams or vendors
- Building a briefing doc out of several research papers on one question
Anywhere “these should agree but don’t” shows up in your work, this prompt shape belongs there.
Prompt of the Day: Same Answer, Different Reasons
The original poster flagged one gap they still handle by hand: sources that land on the same conclusion but get there through different logic. That’s worth its own follow-up prompt.
Looking at the points where the sources agree, check whether they agree for the same reason. For each point of agreement, state the reasoning each source uses to reach it. If the reasoning differs, say so explicitly, even if the conclusion is identical.
Run this right after the main prompt. You’ll catch the cases where two sources shake hands on the outcome but got there from opposite directions. That’s the kind of agreement that looks solid until someone asks a follow-up question.
Grab both prompts, drop in your own sources, and see what disagreements surface that you’d have smoothed over by hand. The original thread is worth a look too, in case other prompt engineers have added their own fixes for the reasoning-gap problem.
Stop asking the model to “summarize these sources”: make it build a synthesis instead
by u/Prior-Philosophy7352 in PromptEngineering