A colleague's simple observation sparked an important realization. Showing the complete conversation with AI systems, not just final outputs, builds trust with audiences. This includes prompts, responses, failed attempts, and iterative refinements.
The principle extends beyond personal credibility. Full transparency about how AI generates answers addresses a fundamental problem in the field. Users cannot evaluate AI reliability when they see only polished results. Hidden reasoning processes obscure potential errors, biases, or hallucinations that shaped the output.
Radical transparency means publishing the working notes of AI interactions. It reveals how questions shape answers. It shows where the AI struggled or changed direction. It exposes the human judgment calls in prompt engineering and output selection.
This approach benefits multiple stakeholders. Readers gain insight into AI limitations and decision-making. Researchers identify patterns in model behavior. Organizations demonstrate accountability in their use of AI tools.
The practice runs counter to current industry norms, which favor sleek interfaces hiding computational complexity. But the colleague's observation points to something powerful. People trust processes they can witness. They distrust black boxes, no matter how polished the results.
Showing work transforms AI from mysterious oracle into a tool with visible constraints and documented reasoning.