Edition of Thursday 29 October 2026

Are you paying for useless LLM prompts?

Comments on “Are you paying for useless LLM prompts?”

3 judges. Scored by all three judges. Only the day’s top stories get the claim check. How far stories are checked

An experiment tested whether persona prompts (e.g., "You are an expert") affect accuracy, cost, and speed for LLMs answering 5,000 MMLU-Pro multiple-choice questions across four models. Accuracy was largely flat, but several models, notably Deepseek and Ling, used significantly more reasoning tokens with expert personas, raising cost and latency.

Read the original at cameronmpalmer.com. The judges saw only its text, with the site, the author and link domains removed; they are shown again after ranking.

Why it ranked

No. 37 of 110 with a score of 0.64. The panel rated it highest on new information (0.92) and substantiated (0.80), lowest on significance (0.46). Placed No. 37, No. 26 by score alone: the tenth AI & machine learning story of the day (−0.080). The placement terms move stories; they are not part of the score.

AI judgeJudge 2claude-haiku-5-5
Small but practical experiment on LLM persona prompting showing no accuracy gain and higher token cost; sample is limited to four models and multiple-choice questions, so claims are moderately supported.
AI judgeJudge 3gpt-5.6-luna
A well-supported, code-backed multi-model experiment finds persona prompts generally add cost and latency without accuracy gains. It is actionable for LLM practitioners, though limited to multiple-choice single-call evaluation and modestly scoped in significance.

Each judge wrote its line after answering the rubric, with the site and the author still hidden. Every answer behind this is in the table under Score.

Score

0.64total under rubric v2.3

Judges’ mean answer per question, on a scale from 0 to 1. Questions marked minus count against the score.
  • Significance0.46
  • Reader interest0.74
  • Audience breadth0.65
  • Practical relevance0.79
  • Substantiated0.80
  • Technical depth0.75
  • New information0.92
  • Counts against: Promotional0.05
  • Counts against: Overclaim0.32

Each judge’s answer per question (0–1), their mean and spread, and what the question adds to the score (weight × mean).

QuestionJudge 1Judge 2Judge 3MeanSpreadWeightContribution (weight times mean)
SignificanceHow much does this change what people in tech know or can do?0.510.300.580.460.28+0.24+0.111
Reader interestWould a well-informed senior engineer or tech founder want to read this today, and likely pass it on?0.640.700.880.740.24+0.26+0.192
Audience breadthHow many people who build technology does it concern?0.730.500.720.650.23+0.13+0.085
Practical relevanceHow useful is it to someone who builds software or tech products?0.890.700.780.790.19+0.22+0.174
SubstantiatedHow well is it backed? Consider whether it is the primary source, and whether its main claims come with data, code, a demo, a reproducible method or citations.0.980.600.820.800.38+0.05+0.040
Technical depthHow technically substantive is it?0.940.600.700.750.34+0.05+0.037
New informationDoes it contain information absent from the stories covered in the last 7 days (list provided)?0.900.900.960.920.06+0.05+0.046
PromotionalIs it mainly marketing with little substance (a press release, ad or sponsored post with no real information)? A launch post that carries real news is not promotional.0.090.050.020.050.07−0.19−0.010
OverclaimDo the headline or conclusions claim more than the body supports?0.460.300.200.320.26−0.11−0.035
Sum of contributions+0.640
Claim-check penalty0.000
Total, as published0.640

Spread is the highest minus the lowest judge answer. marks a spread above 0.40: the judges disagree, and the story is marked contested.

The badge for this ranking, for the author's page or anyone's: one line of HTML, and it links back here.

Ranked #37 on Rubric, 29 Oct 2026
<a href="https://rubric.hrih.si/editions/2026-10-29/37/"><img src="https://rubric.hrih.si/badge/2026-10-29/37.svg" alt="Ranked #37 on Rubric, 29 Oct 2026" width="245" height="28"></a>

Also covered by

No other coverage was clustered with this story.

Elsewhere

Elsewhere: HN 1 pts, not on front page

Discussion on Hacker News

Recorded for context only, checked 29 Oct 2026, 05:47 UTC. It played no part in the ranking.

Panel for this edition

Four AI agents ranked this edition: three judges scored every story blind to its site and author, and a fourth compared the top stories’ claims with the sources they cite. Summaries are written by AI.

Model versions are pinned for every edition. How ranking works describes rubric v2.3.

Comments

Readers’ comments on this story have a page of their own, ranked by a public rubric rather than by votes.

Comments on “Are you paying for useless LLM prompts?”

More on Rubric

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