A bad source can sink a strong essay or a published paper. These 15 prompts force a structured credibility audit before a source enters your bibliography — covering authority, evidence, recency, bias, and the difference between “primary”, “secondary”, and “third-hand repeated claim”.
Who this is for
Students writing essays, journalists fact-checking pieces, analysts producing reports, researchers building bibliographies, and anyone forwarding “I read that…” claims.
When not to use these prompts
Skip these for clearly canonical sources you have already vetted (a foundational textbook in your field, a primary statute). Skip too if all you have is a URL — paste the actual article content; AI cannot browse.
Prompt anatomy / structure formula
A credibility prompt should always carry six elements:
- Role: who the AI plays — research tutor, peer reviewer, exam coach, debate partner, librarian.
- Context: your level, subject, deadline, paper count, target citation style, course or program.
- Goal: one concrete deliverable — 12 quiz items, a 1-page lit matrix, 5 counter-arguments, a 4-week revision plan.
- Constraints: word count, depth, source types allowed, what to skip, what to never claim.
- Output format: numbered list, table, JSON, or graded blocks (E / M / H) so you can paste into Notion / Anki / Word.
- Examples / signal: 1-2 reference paragraphs or anti-examples (“not the way Wikipedia explains it”).
Best for
- Essay bibliographies
- Journalism fact-checking pipelines
- Policy / market research reports
- Academic literature pre-screen
- Personal “is this Twitter thread true” sanity check
15 copy-ready prompt templates
1. CRAAP test prompt (Currency / Relevance / Authority / Accuracy / Purpose)
Quick general-purpose audit; good first pass.
You are an information-literacy librarian. Run the CRAAP test on the source below. For each of the 5 dimensions, score 1-5 with 1 sentence of evidence. End with one of: "use", "use with caveat", "do not use".
Source: {title, author, outlet, date, URL}
Content: {paste}Variables to swap: title, author, outlet, date, URL, content
Optimization: If output is too soft, add: “Treat a score of 3 as fail-by-default. Be ruthless on Authority and Purpose.”
2. Author authority check
Audit the author of this source for authority on the specific claim ({claim}). Cover: relevant credentials, prior publications on this topic, institutional affiliation, conflicts of interest. If author info is missing, flag as a yellow card.
Source: {paste source + author bio}3. Primary vs secondary vs hearsay tracer
For each claim in the source below, classify it as: (a) primary (data/experiment/firsthand observation), (b) secondary (synthesis citing primaries), (c) third-hand (repeats a claim without citing primaries). Output as a table: claim | classification | nearest primary source if any.
{paste}4. Evidence-base audit
List every factual claim in the source below. For each: is supporting evidence provided in the source itself? If yes, what type (study, dataset, anecdote, expert quote, "research shows")? If no, mark as unsupported.
{paste}5. Citation-chain trace
The source claims "{claim}" and cites {Reference X}. Help me trace it: what would Reference X likely say if I read it, what to look for to verify it is being represented accurately, what would indicate it has been misrepresented.6. Funding / conflict-of-interest screen
Below is a source. Identify funding sources, sponsorships, advertising relationships, or disclosed conflicts. Then assess whether the conclusions align suspiciously with the funder’s interests.
{paste source + masthead / funding section}7. Recency-vs-canon check
The source on {topic} is from {year}. Has the consensus on {claim} changed since then? Name 2-3 newer sources or developments to look for before relying on the original.8. Outlet-bias profile
Profile the outlet {outlet name}: editorial slant (if any), ownership structure, audience, peer-review status, retraction history. Mark which kinds of claims I should accept readily and which to double-check.9. Peer-review status verifier
The source claims to be peer-reviewed. Help me verify: journal impact factor, indexing in {Scopus / Web of Science / PubMed}, predatory-journal red flags (rapid acceptance, no editorial board, suspiciously broad scope, vanity fees).10. Image / quote-mining detector
Below is a quoted passage in the source. Pretend you are reading the original; what context, qualifications, or counter-evidence might have been removed in the quote? List 3 things to look for in the original.
{paste quote + surrounding sentences}11. Wikipedia-as-canary
The Wikipedia article on {topic} says: "{paste excerpt}". Trace the citation it relies on, then assess: is this a well-summarized primary source, a circular citation, or a "citation needed" weak spot? Recommend whether to trust this statement.12. Cross-source corroboration
Find 2-3 independent sources that corroborate or contradict the central claim "{claim}". For each: name source, summarize position, note independence (different funder, different lab, different country). Conclude: claim status (well-supported, contested, isolated).13. AI / blog-of-blogs detection
Audit this source for signs it is AI-generated content farm material or a "blog of blogs" with no original reporting: vague author bio, no source URLs, recycled paragraphs, suspiciously broad topic coverage, repeated phrasings.
{paste source}14. Statistical-claim audit
The source uses these statistics: {paste statistics}. For each: original source if any, sample size, methodology, time period, any obvious distortions (cherry-picked baseline, missing denominator, scale tricks). Flag any I should not cite without verification.15. Final-call bibliography note
Write a 4-line bibliography annotation for {source} covering: (a) what it claims, (b) credibility level (high / medium / use-with-caveat), (c) what to verify if I keep using it, (d) what to cite instead if I drop it.Common mistakes
- Treating “found on Google Scholar” as automatic credibility — predatory journals appear there too.
- Stopping at the abstract or headline — the credibility issues usually hide in methods or funding sections.
- Quoting a secondary source as if primary — always trace back one citation level.
- Ignoring date — a 2009 source on AI ethics is closer to ancient than current.
- Confusing “many sources say” with “many independent sources confirm” — citation cascades produce the illusion of consensus.
- Skipping funding disclosure on industry-sponsored research, then being surprised by alignment with funder interests.
- Letting AI declare “credible” without traceable evidence; require a citation or “cannot verify” answer.
How to push results further
- Always paste the actual content; do not pass just a URL — AI cannot fetch reliably.
- Run the same source through 3 templates (CRAAP, primary-vs-secondary, recency); confidence rises when results agree.
- For numerical claims, trace the chain at least 2 levels back; “the WSJ said the WHO said…” is brittle.
- When the source disagrees with your prior, audit harder — that is exactly when bias is most likely.
- Build a “do not cite” personal list with one-line reasons; keep it in your notes for future sourcing.
- Use the AI as a screener, not a verdict — final call sits with you.
- For high-stakes citations (thesis, published article, court filing), pair AI screen with a human librarian or fact-checker.
FAQ
- Can AI tell me if a source is credible?: It can perform a structured audit if you paste the content. It cannot vouch for things it has not seen.
- Is a peer-reviewed paper always credible?: No. Predatory journals exist; flawed peer review exists. Use template 9.
- What is the fastest single check I can run?: CRAAP (template 1) plus primary-vs-secondary tracer (template 3). Together they catch 80% of weak sources.
- Should I cite Wikipedia?: Cite the underlying sources Wikipedia points to, not Wikipedia itself. Template 11 helps you trace.
- What if the source is paywalled?: Get the full text through your library or interlibrary loan; never audit from abstract alone.