Should I use a foundation model or train my own?

Use foundation models unless you have unique requirements, massive proprietary data, and significant ML expertise. For most businesses, building on foundation models via APIs or fine-tuning is more practical.

What's the difference between foundation models and LLMs?

LLMs are a type of foundation model focused on language. Foundation model is the broader category including vision models, multimodal models, and other large-scale pre-trained systems.

Are foundation models biased?

They can reflect biases in training data. Providers work to mitigate this, but it's not eliminated. For sensitive applications, test for bias and implement safeguards.

How do I customise a foundation model for my business?

Options from simplest to most complex: prompt engineering, RAG (adding your knowledge), fine-tuning (training on your data), or building custom applications on top.

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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."

Frequently Asked Questions

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