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Humanising LLM Outputs Is Dumb
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Overview
Humanizing outputs from large language models (LLMs) is viewed as an ineffective method to address verbosity and quirks in their responses. Trends in AI tools, such as the popularity of repositories focusing on ADHD-friendly instructions and simplified technical English, indicate a cultural shift in how users interact with LLMs.
Summary from kuber.studio.
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Humanising LLM Outputs Is Dumb
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Based on 5 sampled comments from this thread. Analyzed 2026-08-11.
- Praise: Some users appreciate the ability to customize their prompts to get more concise and analytical responses from LLMs, which enhances the clarity of outputs.
- Praise: The flexibility in prompt design allows users to tailor interactions, making LLMs feel more like tools designed for specific tasks rather than overly friendly entities.
- Concern: Commenters are frustrated with overly verbose LLM outputs that complicate understanding, often leading to confusion and the need for repeated readings.
- Concern: There's a strong sentiment that LLMs are becoming too human-like, which can blur the line between a tool and something that feels overly personal, leading to discomfort.
- Concern: Several users express skepticism about the quality of LLM outputs, suggesting that the insertion of unnecessary complexity and jargon results in 'blithering' text that lacks substance.