Day 18 - Foundation Models vs Large Language Models (LLMs): What’s the Difference?

Context:

I recently came across the term Foundation Models and wanted to understand how they relate to Large Language Models (LLMs). Are they the same or different?

What I Learned:

• Foundation Models:
  • Large, general-purpose models trained on diverse data types (text, images, audio, etc.).
  • Examples: GPT, CLIP, DALL·E, Whisper.
• LLMs (Large Language Models):
  • A specialized type of foundation model focused only on language tasks (text).
  • Examples: GPT, Claude, LLaMA, PaLM, Gemini.
• Key Relationship:
  • Every LLM is a Foundation Model, but not every Foundation Model is an LLM.
• Example:
  • Mistral Medium 3 is both:
    • An LLM (specialized in text/language tasks).
    • A Foundation Model (broad, adaptable, and part of the foundation model family).

Why It Matters for QA / AI Testing:

• Understanding these categories helps testers choose the right AI tools for tasks like test automation, documentation analysis, or multimodal testing.
• Foundation models enable cross-domain testing (text + image), while LLMs focus on language-centric workflows.
• Knowing the scope of each model prevents mismatched expectations during AI integration.

My Takeaway:

LLMs are a subset of Foundation Models — specialized for language, but built on the same principles of large-scale, general-purpose AI.