Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·25d ago·5 min read
From Prompts to Contracts: Harness Engineering for Auditable Enterprise LLM Agents
TL;DR
Enterprise LLM (Large Language Model) agents can be made auditable by transforming prompts into formal contracts. This approach enhances accountability and traceability in AI interactions.
✦ Why It Matters
Engineers can implement formal contract frameworks for LLMs to enhance compliance and accountability in AI applications today.
Key Takeaways
- Adopt formal contracts to govern LLM interactions.
- Implement auditing mechanisms for AI-generated outputs.
- Ensure compliance with regulatory standards in AI applications.
- Enhance trust in AI systems through transparency.
Full Summary
As organizations increasingly rely on AI, ensuring accountability in AI interactions becomes critical. This work introduces a method to convert user prompts into formal contracts, enabling auditable enterprise LLM agents.
By employing a structured approach that integrates natural language processing with contract formalization, the authors developed a framework that captures user intent and compliance requirements. The methodology includes defining contract templates and utilizing LLMs to generate and validate these contracts.
Results indicate improved traceability and accountability, with a significant reduction in ambiguity during AI interactions. This framework not only enhances compliance but also fosters trust in AI systems, making it easier for organizations to adopt AI responsibly.