How to Stop AI Citing Bad Sources in Research
Your AI research keeps citing blogs that cite other blogs
Ask any AI tool a serious research question and you’ll get a dozen citations back in under two minutes. Check them properly and the picture changes. Four are SEO blog posts quoting each other, two are vendor pages selling the exact thing you asked about and one is a 2019 statistic that’s been copied so often the original study no longer supports it.
That’s a normal hit rate, and it’s why AI research falls apart the moment someone senior asks where a number came from.
Better models haven’t fixed this. Explicit source rules have. The eight prompts below turn a research request into an audit. You tell the AI what counts as evidence before it starts, then you make it grade its own sources afterwards.
Why this keeps happening
Every major model now ships a deep research mode, and they’re all built to look thorough. Twenty sources reads better than four. Nothing in the retrieval step rewards accuracy, so that job lands on you by default.
These prompts work by setting acceptance criteria first, then forcing a second pass where the model marks its own homework against those criteria.
Prompt #1: Source rules brief
What it does: Sets the evidence standard for a research task before the AI searches, so weak sources never enter the output in the first place.
When to use it: As the opening message of any research thread where the output will be seen by someone other than you.
The Prompt:
Apply these source rules for the whole of this task.
Topic: [TOPIC]
Decision this research supports: [WHAT YOU’LL DO WITH THE ANSWER]
Acceptable sources, in order of preference:
Tier 1: peer-reviewed papers, working papers, clinical trials, official datasets
Tier 2: government statistics, regulatory filings, court documents, central bank or agency publications
Tier 3: reporting from outlets with a named author, a publication date and a corrections policy
Tier 4: company documentation, but only for claims about that company’s own product or finances
Never cite: SEO blog posts, listicles, content marketing pages, press releases written up as findings, AI-generated summaries, aggregators that don’t link to the original, anything with no named author, anything published before [DATE LIMIT] unless it is itself the original study.
For every claim you make, give me the claim, its source tier (1 to 4), the publication date and a direct link to the specific page where the claim appears. Not the homepage.
If you can’t find a tier 1 to 3 source for something, write UNSUPPORTED next to the claim and move on. Do not fill the gap with a weaker source.
Research question: [SPECIFIC QUESTION]
How to use it:
Fill in the topic, the decision it supports and the date limit before you paste anything.
Send this on its own and wait for the model to confirm the rules back to you.
Then send your research question, or paste it in at the bottom and send once.
Example input: Topic: remote work and productivity. Decision this supports: a board paper recommending we keep two office days. Date limit: January 2023. Research question: what does the evidence say about output changes when knowledge workers move from full office to hybrid?
What you’ll get: A shorter answer than you’re used to, with tier labels, dates and deep links attached to each claim. Expect two or three UNSUPPORTED tags, which are the most useful lines in the response.
Advanced note: Set the date limit tighter than feels comfortable. If the answer collapses at an 18 month cutoff, the topic moves faster than your research method does.
The rest of the audit is for paying subscribers
Prompt 1 will cut your unusable citations by more than half, mostly because it stops the model papering over gaps.
The other seven prompts are the audit itself. You run those on research you already have, including work the AI did back when nobody was checking.
Prompt 2, Citation grader: makes the model score every source it just handed you and delete its own failures.
Prompt 3, Primary source trace: chases a claim back through the chain of blogs quoting blogs until it reaches the original study or dead-ends.
Prompt 4, Disagreement hunt: finds credible sources that contradict your conclusion, before someone in the meeting does.
Prompt 5, Stale data check: flags figures that have been revised, superseded or quietly withdrawn since publication.
Prompt 6, Who benefits: identifies funding, ownership and commercial interest sitting behind each source.
Prompt 7, Claim-to-source map: lists every claim with nothing attached to it, which is exactly where confident nonsense hides.
Prompt 8, Confidence-rated memo: rewrites the whole piece with high, medium and low confidence labels so you know which lines you can say out loud.
Plus a reusable research brief template you fill in once per project and paste at the top of every thread.
If you only do one thing with AI this week, Prompt 1 is a decent choice. The audit is what makes the output safe to send to someone who checks.