Foundation Model
Large AI models trained on broad data that can be adapted to many downstream tasks. GPT-4, Claude, and BERT are examples that serve as the foundation for specific applications.
In-Depth Explanation
Foundation models are large-scale AI models trained on diverse data that can be adapted to numerous downstream tasks. The term, coined by Stanford researchers, captures how these models serve as the base for many applications.
Characteristics of foundation models:
- Scale: Trained on massive datasets (billions to trillions of tokens)
- Generality: Applicable to many tasks without task-specific training
- Adaptability: Can be fine-tuned or prompted for specific uses
- Emergence: Display capabilities not explicitly trained for
Types of foundation models:
- Language models: GPT-4, Claude, Llama
- Vision models: CLIP, DALL-E, Stable Diffusion
- Multimodal models: GPT-4V, Gemini
- Code models: Codex, StarCoder
Using foundation models:
- Direct prompting (zero-shot, few-shot)
- Fine-tuning for specific domains
- RAG for knowledge grounding
- Building applications on top
Business Context
Foundation models provide powerful capabilities out-of-the-box. Businesses build on them via APIs rather than training from scratch, dramatically reducing AI adoption costs.
How Clever Ops Uses This
We help Australian businesses leverage foundation models effectively - choosing the right model, implementing via APIs, and customising through fine-tuning or RAG when needed.
Example Use Case
"Using Claude as the foundation for a customer service chatbot, customised with company knowledge via RAG without training from scratch."