Generative AI
AI systems that create new content such as text, images, code, audio, or video, rather than simply analyzing or classifying existing data, powered by models like LLMs and diffusion models.
Generative AI is the umbrella term for models that produce new outputs rather than making predictions about existing inputs. This includes LLMs generating text and code, diffusion models creating images, and models synthesizing audio, video, and 3D assets. The common thread is that these models learn the statistical distribution of their training data well enough to sample new, plausible examples from it.
The business impact of generative AI is massive because it automates creative and knowledge work at scale. Content marketing teams use it for drafting blog posts and social media content. Product teams embed it as AI assistants and copilots. Engineering teams use it for code generation and documentation. Customer support teams deploy it for automated response drafting and knowledge base creation.
For growth teams specifically, generative AI creates new categories of growth loops. User activity can trigger AI-generated content that drives SEO traffic. Personalized AI outputs create shareable artifacts that drive viral acquisition. And AI-powered features like smart compose, auto-summarize, and intelligent recommendations increase engagement and retention by making products more valuable with each interaction.
Related Terms
RAG (Retrieval-Augmented Generation)
A technique that grounds LLM responses in external data by retrieving relevant documents at query time and injecting them into the prompt context.
Embeddings
Dense vector representations of text, images, or other data that capture semantic meaning in a high-dimensional space, enabling similarity search and clustering.
Vector Database
A specialized database optimized for storing, indexing, and querying high-dimensional vector embeddings with sub-millisecond similarity search.
LLM (Large Language Model)
A neural network trained on massive text corpora that can generate, understand, and transform natural language for tasks like summarization, classification, and conversation.
Fine-Tuning
The process of further training a pre-trained LLM on a domain-specific dataset to specialize its behavior, style, or knowledge for a particular task.
Prompt Engineering
The practice of designing and iterating on LLM input instructions to reliably produce desired outputs for a specific task.