Foundation Model
Simple Definition
A foundation model is a large AI model trained on a huge and diverse dataset that can be used as a starting point for many different tasks. Instead of building a separate model for every task, developers build one large model and then adapt it.
GPT-4, Claude 3, Gemini, and Llama are all examples of foundation models.
Why “Foundation”?
The name comes from the idea that these models serve as a foundation — you build on top of them rather than starting from scratch for every application.
Before foundation models, most AI systems were trained for a single, narrow task. Now, one large model can handle writing, coding, summarization, translation, and analysis — and be adapted with minimal additional training for specialized uses.
How Foundation Models Are Built
- Pre-training — the model is trained on enormous amounts of text, images, or other data
- Fine-tuning — the pre-trained model is adapted for specific tasks or behaviors
- Deployment — the model is made available via apps or an API
Foundation Models vs. Task-Specific Models
| Foundation Model | Task-Specific Model |
|---|---|
| Trained on diverse data | Trained on one type of data |
| Adaptable to many tasks | Good at one thing |
| Expensive to train, cheap to adapt | Can be cheaper for narrow uses |
| GPT-4, Claude, Gemini | An older spam filter or sentiment classifier |
Related Terms
- LLM — language-focused foundation models
- Fine-Tuning — adapting a foundation model for a specific task
- Generative AI — most generative AI tools are built on foundation models
- Deep Learning — the technique used to train foundation models
See AI terms in action
Browse practical AI workflows that use the concepts in this glossary.
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