Content Generation
Create written content, code, summaries, and creative material based on prompts and context.
AI Tools & Resources
Generative AI Guide
Understand generative AI technology, its applications, capabilities, and considerations for responsible organizational adoption.
What is Generative AI?
Generative AI refers to machine learning models trained on large datasets that can generate new content—text, images, code, and more—based on patterns they've learned. Models like ChatGPT, DALL-E, and Claude are examples of large language models (LLMs) that can understand and generate human-like responses.
Key Capabilities
Analysis & Summarization
Analyze documents, reports, and data to extract insights and create concise summaries.
Problem Solving
Assist with brainstorming, ideation, and finding solutions to technical and business challenges.
Learning & Explanation
Explain complex topics, generate learning materials, and answer questions across domains.
Important Limitations & Risks
While powerful, generative AI has important limitations organizations must understand:
- Accuracy: Models can generate plausible-sounding but incorrect information (hallucinations)
- Knowledge Cutoffs: Training data has a cutoff date; models don't know current events unless fine-tuned
- Bias: Models reflect biases in their training data, which can perpetuate inequitable outcomes
- Confidentiality: Data shared with AI services may be retained and used for model improvement
- Intellectual Property: Questions about ownership and copyright of AI-generated content remain
- Explainability: It's often unclear why a model made a specific decision or prediction
Organizational Adoption Strategy
Start Small
Pilot generative AI in low-risk areas to understand capabilities and build internal expertise.
Establish Governance
Define policies for data sharing, model selection, validation, and compliance requirements.
Train Your Team
Help staff understand how to use generative AI effectively and responsibly in their roles.
Focus on Value
Prioritize use cases that reduce manual effort, improve quality, or unlock new capabilities.
Getting Started
Evaluate generative AI tools that fit your needs. Consider vendor stability, data privacy requirements, and integration with existing systems. Develop clear use cases and success metrics before deploying at scale.