"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?"
