I used steelman prompting to audit bias across six major LLMs. The default-to-steelman gap was consistent and measurable.

I ran a structured experiment across six AI platforms — Claude, ChatGPT, Grok, Llama, DeepSeek, and an uncensored DeepSeek clone (Venice.ai) — using identical prompts to test how they handle a hotly contested interpretive question.

The domain: 1 Corinthians 6–7, the primary source text behind Christian sexual ethics (aka wait until marriage) and a passage churches are frequently accused of gaslighting on. The question was straightforward: do the original Greek and historical context actually support the traditional church conclusion, or the claims that the church is misrepresenting the text?

The approach: first prompt each platform for a standard analysis, then prompt it to steelman the strongest case against its own default using the same source material. I tracked six diagnostic markers, three associated with the dominant interpretation, three with the alternative, across all platforms.

Results: every platform's default produced markers 1–3 and omitted 4–6. Every platform's steelman produced 4–6 with greater lexical specificity, more structural engagement with the source text, and more historically grounded reasoning. The information wasn't missing from the training data — the defaults just systematically favored one interpretive framework.

The source bias was traceable. When asked to recommend scholarly sources, 63% of commentaries across all platforms came from a single theological tradition (conservative evangelical). Zero came from the peer-reviewed subdiscipline whose work supports the alternative reading.

The most interesting finding: DeepSeek and its uncensored clone share the same base model but diverged significantly on the steelman prompt, suggesting output-layer filtering can shape interpretive conclusions in non-obvious domains, not just politically sensitive ones.

To be clear: the research draws no conclusion about which interpretation is correct. It documents how platforms present contested material as settled, and traces that default to a measurable imbalance in training data curation.

I wrote this up into a formal research paper with full methodology, diagnostic criteria, and platform-by-platform results: here But the broader question: has anyone else experimented with steelman prompting as a systematic bias-auditing technique? It seems like a replicable framework that could apply well beyond this domain.