Enterprise-wide, organizations have found that prompt engineering offers one of the greatest returns on investment (ROI) within Generative AI.
Prompt engineering allows organizations to improve the accuracy of their AI-generated output by 20-50%, while simultaneously reducing their inference costs by 35% with no need for retraining their existing models.
Today’s leading organizations are therefore establishing and governing prompt engineering as a formalized, repeatable enterprise-wide discipline. It helps them to improve the reliability of their GenAI solutions, reduce the operational waste generated from their use of GenAI solutions, and minimize their exposure to GenAI-related security, compliance, and prompt-injection risks.
And all this while they scale the safe use of GenAI across multiple teams. The whitepaper will provide information to assist enterprises in:
- Establishing prompt engineering as a formalized and repeatable discipline to replace its current ad-hoc status.
- Improving the consistency and reliability of GenAI outputs across business functions.
- Reducing operational friction through the standardization of prompt design across teams and workflows.
- Strengthening governance through the inclusion of GenAI-specific security, compliance, and risk considerations early in the GenAI development process.
- Clarifying when prompt engineering may be sufficient and when further training through fine-tuning or re-architecturing (RAG) is required.
The content outlines an operating model for responsible enterprise-wide deployment of Generative AI and demonstrates how the disciplined use of prompt engineering enables organizations to achieve faster time-to-value, predictability of AI behaviors, and sustained long-term adoption of GenAI solutions.
It also talks about positioning prompts as a strategic enterprise-wide asset rather than an informal technical workaround.
