A technical presentation on General Relativity generated by a large language model was found to contain subtle but fundamental errors, despite appearing fluent and well-structured. The author developed a multi-agent system to address this, incorporating structured JSON output, deterministic validation rules akin to a "physics linter," and a critic agent to refine the content. While not achieving perfection, this system made correctness measurable and demonstrated that reliable AI output is a system design challenge rather than solely a prompting issue. AI
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IMPACT Highlights the challenge of ensuring factual accuracy in LLM-generated technical content, suggesting system design over prompting for reliable outputs.
RANK_REASON The article describes an experiment and a system design to address a specific technical challenge with LLM-generated content, which is a form of research. [lever_c_demoted from research: ic=1 ai=1.0]