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Large Language Models Explain Experts Better Than Experts Themselves
- Source
- arXiv — Computers and Society
- Published
- Last verified
- 11 Aug 2026
- Confidence
- High
- Evidence
- Original document retained
- Reading time
- 1 min
- Country
- International
- Relevant to
- Research & Evidence, Technology & Data
Executive summary
What happened, and why should leadership care?
A recent study investigates the capability of Large Language Models (LLMs) to externalize tacit knowledge from expert behaviors. The research indicates that LLM-generated knowledge can enhance decision-making quality and enable novices to achieve near-expert performance. This suggests a significant potential for LLMs in knowledge transfer and retention, addressing the long-standing challenge of articulating and preserving expert 'know-how'.
Why this matters
Why is this strategically important?
The ability of LLMs to externalize and transfer tacit knowledge has profound implications for organizational learning, workforce development, and operational efficiency. It offers a potential solution to the critical problem of knowledge loss when experienced personnel depart, enabling more robust knowledge management strategies.
Key insights
What should be noted from the evidence?
- Tacit knowledge, the 'know-how' derived from experience, is inherently difficult for experts to articulate and often poorly documented.
- The study explored whether LLMs can externalize tacit knowledge from observed expert behaviors.
- LLM-externalized tacit knowledge improved the quality of downstream decision-making.
- Novices utilizing LLM-externalized knowledge approached expert-level performance.
- LLM-externalized knowledge often surpassed the effectiveness of knowledge articulated by human experts.
Evidence and confidence
How far can this assessment be trusted?
High confidence. Named institution, original document retained and analysis corroborated.
Analysis is prepared editorially by Aziz Shuaib Ausi. The original publication remains the authoritative record, and executive judgement remains entirely human.
Source
Where does this originate?
Reported by arXiv — Computers and Society · International. This briefing summarises the publication for executive use; the document itself is not reproduced here.
Read the original publication