Apply large language models
Apply large language models, image generators, and AI code assistants to accelerate professional knowledge work and reduce time-to-output in organizational settings
Evaluate the capabilities, limitations, and appropriate use
Evaluate the capabilities, limitations, and appropriate use cases of major generative AI platforms when making organizational technology deployment decisions
Design generative AI-assisted workflows
Design generative AI-assisted workflows that enhance human productivity and creativity while maintaining quality standards and organizational oversight
Implement content generation pipelines
Implement content generation pipelines that combine generative AI capabilities with structured human review for professional publishing and communication
Apply multimodal AI generation techniques to produce
Apply multimodal AI generation techniques to produce and repurpose content across text, image, audio, and data formats for diverse organizational needs
Assess intellectual property, copyright, and attribution
Assess intellectual property, copyright, and attribution risks associated with generative AI outputs when deploying in commercial or public-facing contexts
Configure and fine-tune generative AI tools
Configure and fine-tune generative AI tools using organization-specific style guides, domain knowledge, and quality benchmarks for contextual accuracy
Evaluate AI-generated content quality
Evaluate AI-generated content quality using criteria including factual accuracy, logical coherence, tone alignment, and potential bias indicators
Integrate generative AI capabilities
Integrate generative AI capabilities into existing organizational tools and platforms via APIs, webhooks, and workflow automation configurations
Develop responsible use guidelines and governance policies
Develop responsible use guidelines and governance policies for generative AI deployment that protect organizational integrity and stakeholder trust