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Cookbook: Double-checking citations

[ cookbook ][ updated 2026-09-17 ][ confidence high ][ jev-1.13.0 ][ python sdk 0.6.0 ]#cookbook · citation-check · choice · confidence · verification

TL;DR Verify LLM citations in two steps: an exact (whitespace/curly-quote normalized) substring match against the source marks quotes that aren't there as fabricated; every surviving citation goes to a single Choice question — supports / contradicts / says_nothing — over {"claim": ..., "section": ...}. Map the choice to a verdict and gate on confidence >= 0.8 for auto-accept vs. human review.

Goal

An LLM answers a question and attaches citations: for each claim, a section of a source document and the quote it rests on. Some of those citations are wrong or hallucinated — "the quote can be missing from the document altogether, or sit in it word for word while its context says the opposite of the claim."

The cookbook builds check_citation(), which takes a source document plus one citation and returns one of four verdicts: verified, unsupported, contradicted, or fabricated, plus a confidence that flags the ones a human should look at.

Inputs / state shape

The source is RFC 7519 (JSON Web Token), committed next to the cookbook as rfc7519.txt and split into numbered sections. Citations live in citations.json; four are accurate and four were edited to fail the check.

A citation with a quote:

{
  "id": "aud_reject",
  "claim": "If a validator does not find itself in a token's audience list, it has to reject the token.",
  "quote": "If the principal processing the claim does not identify itself with a value in the \"aud\" claim when this claim is present, then the JWT MUST be rejected.",
  "section": "4.1.3"
}

A claim-only citation has "quote": null and just names a section (e.g. iat_future, section 4.1.6).

The state sent to Jev has exactly two keys:

state={"claim": claim, "section": section}

where section is the full text of the section that contains the quote (found by the string match), not the quote itself. For claim-only citations it is the text of the section the citation names.

Reported corpus stats: 58,365 characters, 45 numbered sections, 8 citations.

Questions asked

One Choice question, verbatim from the cookbook:

QUESTIONS = {
    "relation": Choice(
        instructions="How does the section relate to the claim?",
        criteria={
            "supports": "The section states the claim or directly implies that it is true",
            "contradicts": "The section states the opposite of the claim or implies it is false",
            "says_nothing": "The section does not address what the claim asserts, either way",
        },
    ),
}

Choice.criteria is a dict of option name → description in both SDK 0.5.7 and 0.6.0 — only Score.criteria changed to an ordered sequence in 0.6.0, so this block is unchanged on 0.6.0.

Combining logic in code

Setup (cookbook uses TYPESAFE_MODEL = "jev-1.12" and AUTO_ACCEPT = 0.8, "start high for more human review as you build trust in the model"):

import json, os, re
from pathlib import Path
from time import perf_counter
from typesafe_sdk import Choice, TypeSafeClient

TYPESAFE_MODEL = "jev-1.12"
AUTO_ACCEPT = 0.8

client = TypeSafeClient(
    api_key=os.environ.get("TYPESAFE_API_KEY", "cache-only"),
    base_url=os.environ.get("TYPESAFE_ENDPOINT"),
    timeout=120.0,
)

Step 1 — locate the quote (no model involved):

def normalize(text: str) -> str:
    """Collapse whitespace and fold curly quotes, so a quote matches across line wraps."""
    table = str.maketrans({"“": '"', "”": '"', "‘": "'", "’": "'"})
    return re.sub(r"\s+", " ", text.translate(table)).strip()


def find_quote(sections: dict[str, str], quote: str) -> str | None:
    """The number of the section that contains the quote verbatim, or None."""
    needle = normalize(quote)
    for number in sorted(sections, key=lambda n: [int(p) for p in n.split(".")]):
        if needle in normalize(sections[number]):
            return number
    return None


def locate(sections: dict[str, str], citation: dict) -> tuple[str, str | None]:
    """Step 1 for one citation: a status, plus the section step 2 will read."""
    if citation["quote"] is None:
        return "section-only", sections[citation["section"]]
    number = find_quote(sections, citation["quote"])
    if number is None:
        return "missing", None
    return "found", sections[number]

Step 2 — ask, then fold both steps into one verdict:

RELATION_TO_VERDICT = {
    "supports": "verified",
    "contradicts": "contradicted",
    "says_nothing": "unsupported",
}


def ask(claim: str, section: str) -> dict:
    started = perf_counter()
    response = client.system_one(
        state={"claim": claim, "section": section},
        questions=QUESTIONS,
        model=TYPESAFE_MODEL,
    )
    answer = response.answers["relation"]
    return {
        "choice": answer.choice,
        "probabilities": answer.probabilities,
        "confidence": answer.confidence,
        "seconds": round(perf_counter() - started, 2),
        "input_tokens": response.usage.input_tokens or 0,
        "output_tokens": response.usage.output_tokens or 0,
    }


def verdict(status: str, answer: dict | None) -> dict:
    """Fold step 1 and step 2 into one of the four labels, plus an auto-or-review flag."""
    if status == "missing":
        # confidence None: no model was called, so there is no model confidence to report
        return {"verdict": "fabricated", "confidence": None, "auto": True}
    return {
        "verdict": RELATION_TO_VERDICT[answer["choice"]],
        "confidence": answer["confidence"],
        "auto": answer["confidence"] >= AUTO_ACCEPT,
    }


def check_citation(sections: dict[str, str], citation: dict) -> dict:
    status, section = locate(sections, citation)
    answer = ask(citation["claim"], section) if section is not None else None
    return {"id": citation["id"], "status": status, "answer": answer, **verdict(status, answer)}

The confidence gate: at or above 0.8 the verdict stands on its own; below 0.8 a human confirms it before anything acts on it. See Confidence-gated routing.

Results the cookbook reports

Numbers came from jev-1.12 on 2026-08-16 (TypeSafe's run, replayed from a shipped json_cache.json).

Step 1 statuses: seven found, one missing (sig_reporting), one section-only (iat_future) — the found sections ranged from 529 to 3,122 characters.

Full run over the eight citations:

citation quote relation conf verdict action
epoch_seconds found supports 0.93 verified auto
aud_reject found supports 0.95 verified auto
sig_reporting missing – – fabricated auto
clock_skew found supports 0.99 verified auto
exp_required found contradicts 0.99 contradicted auto
pii_encryption found says_nothing 0.27 unsupported review
iat_future section-only says_nothing 0.56 unsupported review
duplicate_names found supports 0.99 verified auto

TypeSafe's reading of the run: the four accurate citations all came back verified at confidence 0.93 or higher; sig_reporting never reached the model because its quote is not in the RFC; exp_required quotes section 4.1.4 word for word while the same section says "Use of this claim is OPTIONAL", so it is contradicted at 0.99; pii_encryption and iat_future came back unsupported at 0.27 and 0.56, both under the threshold, so both went to a human. pii_encryption is the case that shows why the string match is not enough — its quote is in the source verbatim, but its section says nothing about the claim.

Adapting it to a new domain

Gotchas

Related

Sources