Strategy
Foundation Model
A foundation model is a large AI model trained on broad, general-purpose data that can be adapted to many downstream tasks through prompting, fine-tuning, or RAG. Examples include GPT-4, Claude, and Gemini. For enterprises, the choice of foundation model determines capability ceiling, data-handling guarantees, compliance posture, and per-token cost.
Foundation models are the infrastructure layer of the AI era — analogous to cloud computing platforms. Most enterprise AI deployments are built on top of one or more foundation models rather than training from scratch, which would require billions of dollars and petabytes of data.
Related terms
- LLM (Large Language Model) — A large language model (LLM) is a neural network trained on broad text corpora that can generate, summarize, translate, classify, and reason about natural language.
- Fine-Tuning — Fine-tuning is the process of training an existing AI model on additional task-specific data so its weights adapt to a narrower domain.
- RAG (Retrieval-Augmented Generation) — RAG is a pattern in which an AI model retrieves relevant documents from a knowledge base at query time and uses them as additional context to generate its response.