Generative AI

Generative AI refers to artificial intelligence that creates new content rather than just analyzing, classifying, or predicting from existing data.

What generative AI can create:

• Text (articles, emails, code, poetry)
• Images (art, photos, designs)
• Audio (music, speech, sound effects)
• Video (clips, animations)
• 3D models and designs
• Synthetic data

How it works: Generative AI learns patterns from massive datasets, then uses those patterns to produce new content that resembles—but isn't copied from—the training data.

Examples include ChatGPT (text), DALL-E and Midjourney (images), Suno (music), and Runway (video).

Large Language Models (LLMs) Generate text: conversations, articles, code, summaries. Examples: GPT-4, Claude, Llama.

Image generation models Create images from text descriptions. Examples: DALL-E, Midjourney, Stable Diffusion.

Audio generation Create speech, music, or sound effects. Examples: ElevenLabs (voice), Suno (music), OpenAI Voice.

Video generation Create or edit video content. Examples: Runway, Pika, Sora.

Code generation Write, complete, and explain code. Examples: GitHub Copilot, Claude, GPT-4.

Multimodal models Handle multiple types of content. GPT-4o can process and generate text, images, and audio.

Content creation Marketing copy, social media posts, product descriptions, blog articles—created faster and at scale.

Customer service AI chatbots and agents that understand context and generate helpful, natural responses.

Software development Code generation, documentation, bug fixing, code review acceleration.

Design and creative Concept art, marketing visuals, product mockups, video content.

Personalization Tailored content for each customer—emails, recommendations, experiences.

Research and analysis Summarizing documents, synthesizing research, generating reports.

Training and education Custom learning content, interactive tutorials, practice materials.

Productivity Email drafting, meeting summaries, document generation, data analysis.

Accuracy issues Generative AI can produce plausible-sounding but incorrect information (hallucinations).

Quality inconsistency Outputs vary. Great results sometimes, mediocre ones other times.

Copyright concerns Generated content may inadvertently copy training data. Legal landscape is evolving.

Bias Training data biases appear in outputs. Requires monitoring and mitigation.

Detection Hard to distinguish AI-generated content from human-created content.

Resource requirements Large models require significant compute for training and inference.

Control Difficult to precisely control outputs. Prompt engineering helps but isn't perfect.

Start with clear objectives What do you want to create? What quality matters? What's the use case?

Iterate on prompts First outputs are rarely final. Refine prompts based on results.

Human review Don't publish AI outputs without human review, especially for accuracy-critical content.

Combine with human creativity Use AI for drafts and inspiration; humans for refinement and judgment.

Build guardrails Establish guidelines for AI use. What requires review? What's off-limits?

Stay current Capabilities evolve rapidly. What was impossible last year might be easy now.

Consider ethics Be transparent about AI use. Consider impact on jobs, creativity, authenticity.