Four sources on the same topic, one prompt, zero blending. Paste your sources, label them, and the model keeps them separate. You get to see where they actually agree and where they quietly contradict each other.
A Redditor over on r/PromptEngineering, u/Gloomy-Adeptness-273, got tired of the usual move. Paste a few articles, ask for a summary, and watch the model average everything into one paragraph where nobody’s specific claim survives. So they built a prompt that forces the model to keep score instead of blending.
🧩 The Problem With Normal Summaries
Ask most models to summarize three sources and you get one smooth paragraph. It reads clean, but it hides the actual work. You can’t tell which source said what, which claims only showed up once, or where two sources flatly disagreed.
That’s the part that matters most. A summary that erases disagreement isn’t saving you time, it’s hiding the one thing worth knowing. Three sources rarely say the exact same thing, and the gap between them is usually where the real story lives.
The original poster built their prompt around exactly that gap. Instead of asking for the smoothest possible answer, it asks for the messiest true one first, and only cleans up at the very end.
How The Prompt Is Structured
The prompt labels each source (A, B, C) and runs five fixed steps. State the shared question, list where the sources agree, list where they disagree, flag single-source claims, then summarize what a careful reader could conclude.
Order matters here. Agreement and disagreement get evaluated before any conclusion gets written. That way the model can’t jump straight to a tidy answer and quietly erase the mess it’s built on.
Step four is the sneaky useful one. Any claim that only shows up in one source gets flagged as something to treat with caution. That’s the exact thing that sounds authoritative in a summary but was never actually confirmed anywhere else. Most people never catch that kind of claim, because it reads just as confidently as the ones three sources agree on.
The last instruction in the prompt does the real enforcement work: never merge two sources into one claim without saying so. That single line is what keeps the whole structure from collapsing back into the usual mushy summary halfway through.
🎯 Where This Actually Helps
- Comparing product reviews or vendor claims before a purchase decision. You can see which selling points every reviewer backs up and which ones only came from the company blog.
- Reading three analyst takes on the same market move and seeing exactly where they split, not one blended take that quietly picks a side.
- Digging through research papers on a topic where the “consensus” summary usually hides real debate between the actual studies.
- Fact-checking a claim across news sources before repeating it anywhere, since the single-source flag catches the detail only one outlet reported.
One commenter, u/Nice-Revolution-9560, added a fix worth stealing. Tell the model explicitly to treat each source as a separate document, and to hold off on synthesizing until the final step. Labeling the sources helps, but that extra line is what stops the model from “helpfully” merging them early anyway.
Prompt Of The Day
I’m going to give you several sources on the same topic. Build a research brief.
Sources (labeled):
[SOURCE A: paste]
[SOURCE B: paste]
[SOURCE C: paste]Do this:
- State the question all these sources are trying to answer.
- List the points where the sources AGREE (cite which sources).
- List where they DISAGREE or contradict each other (cite which, and what the disagreement actually is).
- Note what each source claims that no other source backs up (single-source claims to treat with caution).
- End with what a careful reader could reasonably conclude, and what still needs more evidence.
Never merge two sources into one claim without saying so. If a source is thin or opinion-based, say that.
Try adding the separation line from the comments right after “Sources (labeled)”: “Treat each source as a separate document. Do not synthesize until step 5.” It costs one sentence, and it closes most of the gap between “labeled” and “actually kept apart.”
A second variation worth trying: ask for a confidence rating next to each agreed-upon point, high, medium, or low. Base it on how many sources back the point and how directly they say it. That turns the brief from a comparison into something closer to a decision tool.
If you’re feeding in long articles, number the paragraphs inside each source block too. It gives the model something concrete to cite when it flags a disagreement, instead of a vague “Source B implies.”
Head over to the thread on r/PromptEngineering and drop your own version. If you’ve found a cleaner way to stop a model from blending sources it was told to keep separate, that thread wants to hear it.
Frequently Asked Questions
Q: Is labeling sources enough to keep them separate?
Labeling helps, but it’s not perfect on its own. Pair it with an explicit instruction like “treat each source as a separate document, do not synthesize until step 5.” This combination significantly cuts down blending, the label identifies the source, and the delay instruction prevents early merging.
Q: When sources disagree, how do I preserve that insight?
Ask the model to quote the exact sentence from each source when noting disagreements. Without exact quotes, it’ll paraphrase both sides into neutral language and you lose the nuance. Quotes preserve what actually differs and why it matters.
Q: What’s the real value of this structure versus a normal summary?
The agree/disagree split is where insight lives, normal summaries hide it by averaging sources together. This structure forces the model to show you where experts overlap, where they contradict, and what claims nobody else backs up. That’s the signal in the noise.
Here’s a research prompt that pulls several sources into one structured brief without blending them into mush
by u/Gloomy-Adeptness-273 in PromptEngineering