"AI" is a marketing term, not a precise technical definition. What actually exists are functional models designed to solve specific types of problems. Understanding this distinction is crucial for making good technology decisions.
The three big categories
Language Models (LLMs)
Generate and understand text. GPT, Claude, Llama. Good for writing, summarizing, coding, conversations.
Vision Models
Interpret images and video. Object detection, classification, segmentation. OCR, medical diagnostics, quality control.
Generative Models
Create new content from descriptions. DALL-E, Midjourney, Stable Diffusion. Images, music, video.
Why does this distinction matter?
- Avoids overselling: An LLM won't "see" your images unless it has vision capabilities.
- Better decisions: You choose the right tool for the problem.
- Realistic expectations: Each model has clear limitations.
- Correct evaluation: You measure performance on what it was designed to do.
The combination is where the magic happens
The most interesting products combine multiple models: a vision model that interprets an image, an LLM that describes it in natural language, and a generative model that can modify it based on instructions.
Practical considerations
- Cost: LLMs are billed per token, vision models per image, generative per generation.
- Latency: Each model adds time. Chain them carefully.
- Quality: Specialized models often outperform generalist ones in their domain.
- Privacy: Consider where data is processed and stored.
Don't ask "should I use AI?" Ask: "What specific problem do I have and which functional model is best suited to solve it?"
