arXiv:2605.29675v1 Announce Type: cross Abstract: Collaborations with Generative AI often begin with a short prompt and end with an opaque output, leaving implicit who was involved, what task was being pursued, which resources were used, and which constraints should have shaped the process. This limited contextual explicitness hinders trust, traceability, and accountability, particularly when Generative AI is embedded in information-intensive workflows such as search, querying, and profile management. This paper introduces From Prompts to Context, an ontology-driven framework for representing
The proliferation of generative AI in critical workflows necessitates better methods for managing context, trust, and accountability.
Improving the transparency and explicitness of generative AI interactions is crucial for its wider adoption and integration into complex information-intensive systems.
The proposed 'From Prompts to Context' framework aims to shift generative AI from opaque outputs to explicitly contextualized collaborations, enhancing explainability and reliability.
- · AI software developers
- · Enterprises adopting AI
- · AI governance & ethics bodies
- · Users of generative AI
- · Opaque AI systems
- · Companies with limited AI transparency
- · Workflows lacking explicit context
Increased trust and adoption of Generative AI in critical business and information management workflows.
Development of new tools and standards for AI context management and traceability within enterprise systems.
Potential for regulatory frameworks to mandate context-aware AI for critical applications, driving an industry-wide shift.
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Read at arXiv cs.AI