Ai+ Training

In recent years, the deployment of Large Language Models (LLMs) in enterprise environments has surged, providing unprecedented capabilities for natural language understanding and generation. However, the governance of these models, particularly in terms of controlling and ensuring the quality and security of input-output operations, remains a critical concern.

In this session, we introduce a robust framework, developed at Oracle, for augmenting PromptOPS, a prominent orchestration system for LLMs, with integrated solutions from our strategic partners. Our approach involves encapsulating LLMs within a well-defined boundary of operational guardrails, helping to safeguard the integrity, confidentiality, and accountability of data processed through these models. We demonstrate the modular integration of partner products to provide a suite of pre-processing and post-processing tools, that can support sanitized input and output, robust error handling, and compliance with regulatory standards. 

Through extensive evaluations, we exhibit the efficacy of our framework in maintaining the desired operational guardrails while enabling enhanced functionality and scalability in deploying LLMs across various enterprise use-cases. Our contributions present a significant stride towards establishing a secure and controlled operational environment for LLMs, fostering their broader adoption in critical enterprise applications.

Local ODSC chapter in NYC, USA