Framework for Automated Onboarding of Teams to Large Language Models (LLMs) Tools in Large Tech enterprises
Keywords:
Large Language Models, Automated Onboarding, Role-Based Access Control, Identity Management, Infrastructure-as-Code, CI/CD Integration, enterprise AI governance, Prompt Engineering, Data Compliance, Usage TelemetryAbstract
The rapid adoption of Large Language Models (LLM) like GPT-4 and LLaMA-3 in the business processes requires scalable and safe onboarding platforms among the tech users. Some of the big technology companies experience large-scale problems, such as control over proprietary data sets, API rate limits, access-based management (RBAC), and data residency regulations, like GDPR and SOC 2. The present paper offers to implement an automated onboarding solution using Identity and Access Management (IAM) pipelines, Single Sign-On (SSO) procedures, and Infrastructure- as-Code (IaC) templates that allow managing the process of distributing and recalling access to tools facilitating LLMs within the organization in cases when its distributed teams are involved. The framework includes automated prompts engineering tutorials, ongoing usage monitoring via telemetry dashboards, and the attachment of CI/CD pipelines towards the controlled implementation of model changes. An empirical test with a 10,000-user pilot in one of the Fortune 100 companies illustrates a 65 percent decrease in manual provisioning time, 40 percent drop in security incidences, and 30 percent increase in user adoption metrics. It is scalable, developer productivity is improved, and operational overhead is reduced in LLC adoption programs up to enterprise levels.Downloads
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