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LLMs tested on non-existent tool: context proves more critical than model choice

By PulseAugur Editorial · [1 sources] ·

An experiment tested five current-generation LLMs—Claude Opus 4.7, Claude Sonnet 4.6, GPT-5, Gemini 2.5 Pro, and Grok 4—by asking them about a non-existent tool called AuriKey. When given no context, all models hallucinated, with Grok 4 producing the most specific but entirely fabricated details, while Claude models offered more honest, albeit less specific, responses. The significant performance gap observed initially narrowed dramatically when a short, fabricated document about AuriKey was provided as context, highlighting that context engineering is a more impactful factor than model choice for improving LLM responses. AI

IMPACT Highlights the critical role of context engineering over model choice for LLM performance and honesty.

RANK_REASON The item is an opinion piece and experiment analysis by a user, not a direct release or announcement from a frontier lab.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs tested on non-existent tool: context proves more critical than model choice

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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Ken Imoto ·

    5 LLMs Answered the Same Question About a Tool That Doesn't Exist. The Quality Varied 4.6x.

    <h2> The prompt I sent five times </h2> <p>I sent the exact same message to five current-generation LLMs:</p> <blockquote> <p>Tell me about AuriKey's organization management features. Specifically: how do you create an organization, invite users, and manage permissions?</p> </blo…

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