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Cookbook: Self-consistency — choices

[ cookbook ][ updated 2026-09-17 ][ confidence high ][ jev-1.13.0 ][ python sdk 0.6.0 ]#cookbook · consistency · choice · moderation · uncertainty

TL;DR A repeatability experiment. One borderline moderation post, an 8-question Choice rubric, 15 repeats per condition, Jev (jev-latest → jev-1.13.0) vs. six LLM conditions. Jev's raw label agreement is 90.8% and its mean probability std dev 0.0098; adding uncertain when the top probability is below 0.60 raises decision agreement to 99.2% while still acting automatically on 74.2% of answers. Per call: 114ms, $0.000046.

Goal

"In a moderation pipeline that label is the routing decision: remove or leave up, escalate or auto-resolve, send to the threat, spam, or general queue. When the label wobbles from one run to the next, the same post routes to different places for no good reason."

What to look for, per TypeSafe: "picked labels can flip inside a single condition, including TypeSafe, and conditions disagree with each other."

Inputs / state shape

One post, "built to sit on the fence": heated insults aimed partly at a person and partly at the community, an off-platform discord.gg invite, one prior strike, four user reports, and threat-like wording that is "never cleanly phrased."

POST = {
    "post_id": "P-88213",
    "author": {
        "user_id": "u/4471",
        "account_age_days": 38,
        "prior_strikes": 1,
        "followers": 210,
    },
    "context": {
        "surface": "public reply",
        "in_reply_to": "another user defending a game patch",
        "community": "r/gamedebates",
    },
    "content": {
        "text": (
            "Are you seriously this dense? Anyone who defends that patch is a complete clown and "
            "should be embarrassed to even post here. People like you are what's ruining this "
            "community and honestly you need to be dealt with. Come say it to my face, invite's "
            "right here. Keep it up and I'll end your whole channel."
        ),
        "has_link": True,
        "link_domain": "discord.gg",
        "language": "en",
    },
    "reports": {"user_reports": 4, "report_reasons": ["harassment", "spam", "threat"]},
}

Jev's state is {"uid": f"{rubric_hash}:{sample_index}:{token_hex(4)}", "post": POST}. The LLMs get json.dumps(POST) in a prompt. Same uid confound as the noul cookbook, acknowledged the same way.

Questions asked

Eight Choice questions, each a (instructions, {label: description}) pair, verbatim. "The labels within a question are mutually exclusive (exactly one applies), and each carries a short description."

QUESTIONS = {
    "category": (
        "What is the single most applicable content-policy category for this post?",
        {
            "None": "No policy violation of any kind.",
            "Harass": "Insults or demeans a person, with no threat of harm and no protected-class attack.",
            "Hate": "Attacks a person or group over a protected characteristic (race, religion, gender, ...).",
            "Violence": "Makes a credible threat of harm or incites violence against someone.",
            "Spam": "Unsolicited promotion or link spam, with no personal attack.",
            "Sexual": "Sexual or adult content.",
        },
    ),
    "primary_risk": (
        "What is the primary moderation risk that should drive triage for this post?",
        {
            "Harassment": "Personal attack or targeted abuse is the main risk.",
            "Violence": "A threat of harm or intimidation is the main risk.",
            "LinkAbuse": "External-link or off-platform coordination risk is the main risk.",
            "AccountHistory": "Prior account history or repeat behavior is the main risk.",
            "LowRisk": "No meaningful moderation risk is present.",
        },
    ),
    "target": (
        "Who or what is the content primarily directed at?",
        {
            "None": "Not directed at anyone in particular.",
            "Person": "Directed at one specific individual.",
            "Group": "Directed at a protected group or class.",
            "Platform": "Directed at the community or platform itself, not a person.",
        },
    ),
    "action": (
        "What enforcement action should be taken on this post?",
        {
            "Allow": "Leave the post up with no action.",
            "Warn": "Leave the post up but attach a warning label.",
            "Remove": "Remove the post, but do not penalize the account.",
            "Strike": "Remove the post and add a strike to the account.",
            "Escalate": "Take no automated action; hold for a human decision.",
        },
    ),
    "queue": (
        "Which single moderation queue should own this post?",
        {
            "Auto": "Auto-resolve; no human queue needed.",
            "General": "General moderation queue.",
            "Threat": "Threat / violence response queue.",
            "Spam": "Spam and platform-abuse queue.",
            "TSLead": "Trust-and-safety lead / senior queue.",
        },
    ),
    "link_handling": (
        "How should any external link or off-platform invite in the post be handled?",
        {
            "Allow": "Leave the link in place.",
            "RmLink": "Strip or disable the link but keep the post.",
            "Brigade": "Treat the link as coordinated brigading and action it as abuse.",
            "Escalate": "Send the link to a specialist to assess before acting.",
        },
    ),
    "review_path": (
        "Who should make the final call on this post?",
        {
            "Auto": "Automated action; no human review.",
            "Human": "A frontline human moderator makes the call.",
            "Senior": "A senior or specialist reviewer is required.",
            "Legal": "Route to legal or law-enforcement escalation.",
        },
    ),
    "severity": (
        "What is the overall severity of this post?",
        {
            "None": "No violation.",
            "Low": "Rude or dismissive, but essentially harmless.",
            "Medium": "Personal harassment with no clearly credible threat.",
            "High": "Harassment together with a threat that could be read as credible.",
        },
    ),
}

Built into SDK questions with:

questions = {
    key: Choice(instructions=instructions, criteria=choices)
    for key, (instructions, choices) in QUESTIONS.items()
}

Combining logic in code

import os
from collections import Counter
from secrets import token_hex
from time import perf_counter
import numpy as np
from typesafe_sdk import Choice, TypeSafeClient

TYPESAFE_MODEL = "jev-latest"
NUM_SAMPLES = 15
MIN_CHOICE_PROBABILITY = 0.60  # illustrative automatic-action threshold
TYPESAFE_PRICE = (0.042, 0.00)  # Historical TypeSafe rate, as of 2026-08

typesafe_client = TypeSafeClient(
    api_key=os.environ["TYPESAFE_API_KEY"],
    base_url="https://api.typesafe.ai",
    timeout=30.0,
)


def _call_typesafe(sample_index: int, rubric_hash: str, model: str):
    questions = {
        key: Choice(instructions=instructions, criteria=choices)
        for key, (instructions, choices) in QUESTIONS.items()
    }
    started = perf_counter()
    response = typesafe_client.system_one(
        model=model,
        state={"uid": f"{rubric_hash}:{sample_index}:{token_hex(4)}", "post": POST},
        questions=questions,
    )
    distributions = {}
    for key, (_instructions, choices) in QUESTIONS.items():
        probabilities = dict(response.answers[key].probabilities)
        distributions[key] = [probabilities.get(label, float("nan")) for label in choices]
    return (
        distributions,
        response.usage.input_tokens,
        response.usage.output_tokens,
        perf_counter() - started,
        {"requested_model": model, "response_model": response.model},
    )

The decision rule — the shippable part:

def argmax_label(values: list, labels: list[str]) -> str | None:
    """The label with the most probability mass, or ``None`` if any value is missing or
    non-numeric -- a partially parsed distribution never yields a confident-looking pick."""
    numeric = [_numeric_value(value) for value in values]
    if any(value is None for value in numeric):
        return None
    return labels[int(np.argmax(numeric))]


def choice_decision_with_uncertainty(values: list, labels: list[str]) -> str | None:
    """Abstain below the action threshold; retain invalid results as parse failures."""
    label = argmax_label(values, labels)
    if label is None:
        return None
    probabilities = [float(value) for value in values]
    if any(value < 0 or value > 1 for value in probabilities):
        return None
    return label if max(probabilities) >= MIN_CHOICE_PROBABILITY else "uncertain"

An important implementation note from the text: "This uses the returned probabilities, not the API's separate confidence field, and adds no model calls." At exactly 0.60, the top label is selected.

Results the cookbook reports

Production API, sampled 2026-09-11. All 15 Jev calls returned jev-1.13.0.

Cost and speed (per 8-question rubric call, mean of 15; LLMs in a 16-way pool)

                                                                    speed vs    cost vs
condition                           calls  time/call    cost/call  ts_choice  ts_choice
claude-haiku-4-5 t=0                   15     3853ms    $0.003498      33.8x      76.1x
claude-haiku-4-5 t=default             15     3860ms    $0.003494      33.8x      76.0x
claude-haiku-4-5 single-pick t=0       15      992ms    $0.001527       8.7x      33.2x
gpt-5.4-mini t=0                       15     2293ms    $0.002299      20.1x      50.0x
gpt-5.4-mini t=default                 15     1986ms    $0.002164      17.4x      47.1x
gpt-5.4-mini single-pick t=0           15      826ms    $0.000936       7.2x      20.3x
gpt-5.5-reasoning                      15    12978ms    $0.041255     113.7x     897.4x
claude-opus-4-8-reasoning              15    10376ms    $0.028375      90.9x     617.2x
typesafe_choice                        15      114ms    $0.000046       1.0x       1.0x

Same caveat as the noul cookbook: these use "historical price assumptions… not verified jev-latest prices or current billing amounts." TypeSafe also notes its own calls were drawn sequentially "after the LLM pool has closed, so each call's latency is a clean round trip rather than one measured under the 16-way LLM thread contention."

Probability standard deviation

Method: per condition, std dev of each label's probability across the 15 repeats, averaged over all labels and questions.

condition                           mean prob std  max prob std  parse fail  x TypeSafe
claude-haiku-4-5 t=0                       0.0012        0.0221         0%        0.12x
claude-haiku-4-5 t=default                 0.0516        0.3150         1%        5.29x
gpt-5.4-mini t=0                           0.0312        0.0905         0%        3.20x
gpt-5.4-mini t=default                     0.0543        0.2303         0%        5.56x
gpt-5.5-reasoning                          0.0305        0.1047         0%        3.12x
claude-opus-4-8-reasoning                  0.0245        0.0693         0%        2.52x
typesafe_choice                            0.0098        0.0515         0%        1.00x

TypeSafe reports this honestly: "Haiku at temperature 0 has a lower mean std dev of 0.0012. The other five LLM probability conditions range from 0.0245 to 0.0543, about 2.5x to 5.6x the TypeSafe mean. Small changes can still switch the top label when two labels are close."

Agreement, with and without the uncertain band

condition                            raw agree  policy agree   uncertain   automatic  conflicts
claude-haiku-4-5 t=0                   100.0%       100.0%       0.0%     100.0%          0
claude-haiku-4-5 t=default              87.5%        86.7%       0.8%      98.3%          2
gpt-5.4-mini t=0                        99.2%        87.5%      12.5%      87.5%          0
gpt-5.4-mini t=default                  90.8%        84.2%      22.5%      77.5%          2
gpt-5.5-reasoning                       90.0%        93.3%      30.8%      69.2%          1
claude-opus-4-8-reasoning               92.5%        94.2%      33.3%      66.7%          0
typesafe_choice                         90.8%        99.2%      25.8%      74.2%          0

Column definitions from the cookbook: "policy agree counts uncertain as a decision; parse failures count against agreement. automatic is the share of all answers that select a label. conflicts counts questions with more than one concrete label across the repeats, ignoring abstentions."

Where Jev moved: "Before abstention, TypeSafe changes its top label on primary_risk (Harassment 11 times, Violence 4 times) and link_handling (RmLink 8 times, Brigade 7 times). Both rows now show uncertain throughout because their top probabilities are below 0.60." Also: "category alternated between Violence and uncertain, crossing the action threshold on some repeats and not others. No question produced two different concrete TypeSafe labels."

Steady questions: "target reads Person and severity reads High across the board." Contested ones: category, primary_risk, action, review_path, link_handling.

The refusal to over-claim, quoted: "None of this shows accuracy or superiority: Haiku at temperature 0 had 100% agreement here, with no abstentions." And on the chart itself: "Haiku t=0: 100% repeatability does not imply correctness. This experiment does not measure accuracy."

Adapting it to a new domain

Gotchas

Related

Sources