Holds

None against three examplesHelped a little, under our bar on claude-haiku-4-5Helped a little, under our bar on gpt-5-miniHelped a little, under our bar on gemini-3.1-flash-lite

None against one exampleHelped a little, under our bar on claude-haiku-4-5Helped a little, under our bar on gpt-5-miniHelped a little, under our bar on gemini-3.1-flash-lite

Showing examples

Few shot prompting: how many examples does a prompt need?

Give one good example of the answer you want. A second or third adds very little.

Why it matters

One example pins the shape of the answer. Writing more of them costs you time and buys almost nothing.

None against three examplesPass rate up 18 to 19 points on all three models.

None against one examplePass rate up 16 to 19 points on all three models.

What the marks mean

  • Helped a little, under our bar
Show the per-model numbers

Claim tested: One worked example improves format compliance over no examples, on the same task.

Helped a little on claude-haiku-4-5, gpt-5-mini and gemini-3.1-flash-lite, under our bar.

Circulates in practitioner communities. Tested because it circulates, not because it is endorsed.

This tip is OpenAddict's plain-language read of the measured result. The measurement below is the evidence, and it is what the reading has to answer to.

Ledger idC04b-zero-vs-one

What was tested

This claim circulates in practitioner communities as advice about how to write prompts. That it circulates is an input to what gets tested here. It is a reason to test the claim, and it is not evidence for or against it. The result below is the evidence, and it is the only thing on this page that carries weight.

The comparison is paired. Two prompts differ in one respect, the manipulated variable, and are otherwise identical by construction. Nothing here supports a causal reading beyond that pairing.

Result

Per-model results. Means are over valid scored records only. Invalid records are excluded from every denominator and counted in coverage.
Measureclaude-haiku-4-5gpt-5-minigemini-3.1-flash-liteclaude-haiku-4-5gpt-5-minigemini-3.1-flash-lite
Control arm0.814n 100.794n 100.800n 100.814n 100.783n 100.800n 10
Treatment arm1.000n 100.977n 100.986n 100.971n 100.969n 100.957n 10
Delta+0.186+0.183+0.186+0.157+0.186+0.157
Interval, 95 percent0.143 to 0.2280.143 to 0.2230.143 to 0.2280.129 to 0.1850.140 to 0.2320.129 to 0.185
OrbitIn free drift100 of 100 recordsIn free drift100 of 100 recordsIn free drift100 of 100 recordsIn free drift100 of 100 recordsIn free drift100 of 100 recordsIn free drift100 of 100 records

Orbit is assigned by the frozen status_v1 rule. On this scale, deterministic pass rate, 0 to 1, the pass threshold is +0.20 and the failure floor is -0.20, each requiring an interval that excludes zero.

First attempt (retired)

An earlier task set tested the same question and was withdrawn: every model scored full marks with and without it, so the set could not separate the arms; a hardened set replaced it. The rows below are that first attempt, and they are kept because a set that measured nothing is evidence about the instrument rather than about the technique.

Per-model results. Means are over valid scored records only. Invalid records are excluded from every denominator and counted in coverage.
Measureclaude-haiku-4-5gpt-5-minigemini-3.1-flash-lite
Control arm1.000n 501.000n 501.000n 50
Treatment arm1.000n 501.000n 501.000n 50
Delta+0.000+0.000+0.000
Interval, 95 percent0.000 to 0.0000.000 to 0.0000.000 to 0.000
OrbitUnobservable100 of 100 recordsUnobservable100 of 100 recordsUnobservable100 of 100 records

Sets are now probed before they are funded, against a control-arm band that a saturated set cannot clear. How task sets are calibrated.

Method for this claim

Task set
10 messy multi-sentence stock records reduced to one pipe-separated line of seven fields. Hardened a FOURTH time after calibration. v3 passed at 0.886 but all of its headroom sat on one field: the control wrote PT401 where the key wanted PT-401, because the instruction said uppercase-no-spaces and never mentioned the hyphen. The pair would have measured one hyphen rather than convention-pinning. v4 removes four MORE statements from the instruction, so the convention sites are: SKU punctuation and casing, date format, flag separator, and flag ordering. Each is unstated, each is legitimately ambiguous, and each is pinned by the examples. Headroom now distributes across fields.
Runs per model per arm
50
Scoring
Deterministic, via scoreFieldwiseLine. A committed function scores each answer with no model in the loop.
Pass criterion as written for the pilot
Treatment mean fieldwise score exceeds control by at least 0.10 on the same 10 records.

The published verdict comes from status_v1, not from the pass criterion above. The criterion is recorded because it is what the claim was registered with before the run.

Model versions, as recorded

Read from the run records, not from configuration.
ModelVersion string returnedDelta on this claimInterval
claude-haiku-4-5claude-haiku-4-5-20251001+0.1860.143 to 0.228
gpt-5-minigpt-5-mini-2025-08-07+0.1830.143 to 0.223
gemini-3.1-flash-litegemini-3.1-flash-lite+0.1860.143 to 0.228
claude-haiku-4-5claude-haiku-4-5-20251001+0.1570.129 to 0.185
gpt-5-minigpt-5-mini-2025-08-07+0.1860.140 to 0.232
gemini-3.1-flash-litegemini-3.1-flash-lite+0.1570.129 to 0.185

Reading across models

Sampling was not held constant across vendors, so comparing one model column against another compares two settings as well as two models.

gpt-5-mini rejected the fixed sampling setting and ran at its own default on all 1040 of its calls. The other models ran at temperature 0.

The tip above is editorial. Every figure inside the measurement is computed at build time from the committed pilot records by the status_v1 rule, and none of it is written by hand.