One of the most persistent problems with large language models isn't their intelligence—it's their confidence when they don't know something. A developer going by 'kenimo49' on DEV.to recently ran a fascinating experiment to quantify exactly how badly these systems can hallucinate details about software that doesn't exist.

The Experiment Setup

The test was straightforward: ask five current-generation LLMs the same question about organization management features for a tool called 'AuriKey,' which has no public documentation, codebase, or existence. The models tested included Claude Opus 4.7, Claude Sonnet 4.6, GPT-5, and Google's Gemini—some of the most capable systems available today. Each received identical prompts asking how to create an organization, invite users, and manage permissions within this fictional platform.

What the Responses Revealed

The quality gap between the best and worst responses was striking: a 4.6x difference in helpfulness scores. This isn't a minor variance—this is the difference between a response that might save you hours of work and one that'll send you down a rabbit hole chasing features that don't exist. Some models apparently fabricated entire API endpoints, permission hierarchies, and UI workflows with convincing specificity.

Why This Matters for Developers

This experiment cuts to the heart of how developers actually use these tools in 2026. When you're integrating unfamiliar APIs or exploring new frameworks, LLMs often serve as your first interface—and if they can't distinguish between real documentation and their own confident fabrications, you've got a serious problem. A 4.6x quality swing on the same question from the same prompt suggests that model selection matters enormously for reliability-critical work.

The Hallucination Problem Isn't Solved

Despite years of progress in alignment and RLHF, these models still can't reliably tell you 'I don't know' when asked about obscure or nonexistent tools. They fill gaps with plausible-sounding details because that's what they were trained to do—and the commercial pressure to appear helpful actively discourages uncertainty. The real question isn't whether hallucination exists (it obviously does), but how we build guardrails that matter in production environments.

Key Takeaways

  • Model choice significantly impacts reliability when working with unfamiliar software stacks
  • Identical prompts can yield dramatically different quality responses across models
  • Confidence and accuracy don't correlate—confident wrong answers are still dangerous
  • Developers need verification strategies, not just better models

The Bottom Line

This isn't a gotcha moment—it's validation that we can't treat these systems as authoritative knowledge sources. A 4.6x quality gap on hallucination-prone queries means engineering teams need robust fact-checking pipelines before shipping AI-assisted code to production.