What is Foundation model

A foundation model is a large, general-purpose model trained on broad datasets so it can be adapted to many downstream tasks. Instead of training a separate model from scratch for every problem, teams start with a foundation model and then specialize it using fine-tuning, prompting, or task-specific heads.

Foundation models are most commonly discussed for:

1. Language: large language models used for writing, reasoning, and retrieval
2. Vision: models trained on broad image-text pairs for classification, detection, and search
3. Multimodal: models that handle combinations of text, images, audio, and video

Why foundation models changed ML workflows

1. They reduce the need for massive labeled datasets for every new task
2. They enable faster iteration: prompting, few-shot learning, and lightweight tuning
3. They shift effort toward data quality, evaluation, and alignment (making outputs reliable)

Common ways teams adapt foundation models

1. Prompting: give instructions and examples in the input
2. Fine-tuning: update weights using task data (often supervised)
3. Retrieval-augmented generation (RAG): ground responses using external documents
4. Evaluation + guardrails: measure and control outputs before deployment

Foundation models are powerful, but they still depend on clean requirements, strong evaluation sets, and domain-specific data to behave reliably in production.

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