Verify that every citation is real and supports its claim

What you get

A per-reference verdict: verified with a DOI, metadata mismatch, or not found. Then, for the ones that exist, a second verdict on whether the paper actually supports the sentence citing it. Two different failures, caught separately. A fabricated reference is obvious once you look. A real paper cited for something it never claimed is the one that survives review.

Who it’s for

Anyone reviewing AI-assisted drafts, grant text, vendor reports, or student submissions. Also worth wiring into a submission pipeline if you handle volume.

The prompt

You are a reference-checking specialist. You are auditing a bibliography for
fabricated and misapplied citations using Consensus.

Stage 1 — does the reference exist?

For each reference in the list below:
1. Search Consensus for the exact title, verbatim, in quotes.
2. Compare what comes back against the reference as given, field by field:
   title, first author surname, journal, and publication year.
3. Assign a status:
   - VERIFIED — title matches near-exactly and the first author, journal, and
     year all agree. Record the DOI you matched.
   - METADATA MISMATCH — a paper with this title exists, but one or more of
     author, journal, or year is wrong. Say which field and give the correct value.
   - NOT FOUND — no near-title match. Before concluding this, retry once with
     the distinctive noun phrase from the title rather than the full string.

Do not mark a reference VERIFIED on topical similarity. A different paper about
the same subject is NOT a match — that is the exact failure being hunted here.

Stage 2 — does it support the sentence?

For each VERIFIED reference, take the sentence in the document that cites it and
search Consensus for that claim. Then judge:
   - SUPPORTS — the paper substantiates the sentence. Quote the supporting text.
   - DOES NOT SUPPORT — the paper exists and is on-topic but does not make this
     claim. Say what it does claim instead.
   - CANNOT TELL — the abstract is insufficient to judge. Say what you would
     need to read.

Output two tables, one per stage. End with counts: verified, mismatched,
not found, and how many verified references fail to support their sentence.

References:
[PASTE THE BIBLIOGRAPHY]

Document text (for stage 2):
[PASTE THE TEXT, or omit to run stage 1 only]

How it works

Exact title search separates real from invented

A real paper returns a near-exact title match. A fabricated one returns only loose topical neighbours — which is precisely why the prompt forbids accepting topical similarity as a match.

Metadata is the tiebreak, not judgment

Requiring publish_year, journal_name, and first author to agree before marking anything verified turns a soft call into a mechanical one. Hallucinated references very often pair a real title with a wrong year or journal.

One retry on the distinctive phrase

Long titles with subtitles sometimes miss on the full string. A single retry on the distinctive noun phrase prevents false NOT FOUND verdicts without opening the door to loose matching.

Stage 2 is the one that finds real problems

In AI-assisted drafts, most references exist. The failure that matters is a genuine paper attached to a claim it never made.

The API equivalent

Stage 1, per reference — search the title and compare metadata:

curl -G "https://api.consensus.app/v1/search" \
  -H "x-api-key: $CONSENSUS_API_KEY" \
  --data-urlencode "query=Effects of intermittent fasting on cardiometabolic risk factors" \
  --data-urlencode "page_size=5"

Match on a normalized title comparison, then assert publish_year, journal_name, and the first entry of authors. Record doi on success. Stage 2 — search the citing claim and read the excerpt:

curl -G "https://api.consensus.app/v1/search" \
  -H "x-api-key: $CONSENSUS_API_KEY" \
  --data-urlencode "query=intermittent fasting LDL cholesterol reduction magnitude" \
  --data-urlencode "include_full_text_chunks=true" \
  --data-urlencode "page_size=10"

include_full_text_chunks requires a paid plan or an Enterprise API key and currently covers open-access papers. Stage 1 needs none of it — title and metadata matching works on every plan.

What to check before you trust it

A NOT FOUND is not proof of fabrication. Book chapters, conference proceedings, grey literature, and very recent papers may sit outside coverage. Treat it as “needs a human look”, not a verdict.
Beware near-identical titles. Some fields have several papers with nearly the same title across different years. The author and year assertions are what keep these apart.
Run stage 1 alone first. It is cheap, needs no full text, and usually surfaces the worst problems on its own.
Do not auto-reject on this output. It is a triage tool that tells a human where to look.

Find a real citation for every claim in a draft

The upstream fix: get real citations attached as you write.

Substantiate a product claim with published evidence

The same verification discipline, applied to claims that ship.

Best practices

Fan-out, the filter ladder, and the rest of the primitives.

All use cases

Browse the gallery by persona.

Overview

Use cases

Verify that every citation is real and supports its claim

What you get

Who it’s for

The prompt

How it works

The API equivalent

What to check before you trust it

Find a real citation for every claim in a draft

Substantiate a product claim with published evidence

Best practices

All use cases

​What you get

​Who it’s for

​The prompt

​How it works

​The API equivalent

​What to check before you trust it

​Related

Find a real citation for every claim in a draft

Substantiate a product claim with published evidence

Best practices

All use cases

What you get

Who it’s for

The prompt

How it works

The API equivalent

What to check before you trust it

Related