Types of AI Models

Types of AI Models: Choosing the Right Tool for the Job 🔧

Now that we understand what AI models are, let's explore the different types available. Think of this like choosing the right tool from your toolbox—you wouldn't use a hammer to screw in a lightbulb, right?

Classification by Nature: What Can They Do?

AI models can be categorized by their primary function. Let's break this down:

1. Language Models (LLMs)

These are the text wizards we've been talking about. They understand, generate, and manipulate human language.

Examples:

• GPT-4, Claude, LLaMA
• What they're great at: Writing, translation, summarization, coding help
• What they struggle with: Math calculations, real-time data, factual accuracy

2. Computer Vision Models

These are the "eyes" of AI—they process and understand images and videos.

Examples:

• DALL-E, Midjourney, Stable Diffusion
• What they're great at: Image generation, object detection, facial recognition
• What they struggle with: Understanding context, generating coherent text

3. Multimodal Models

The best of both worlds! These can handle text, images, audio, and sometimes video.

Examples:

• GPT-4V, Claude 3.5 Sonnet, Gemini
• What they're great at: Understanding context across different media types
• What they struggle with: Can be more expensive and slower than specialized models

4. Specialized Models

These are built for specific tasks like medical diagnosis, financial analysis, or scientific research.

Examples:

• Medical AI models, financial forecasting models
• What they're great at: Their specific domain (often better than general models)
• What they struggle with: Anything outside their specialty

Classification by License: The Legal Side

This is where things get interesting (and sometimes complicated). AI models come with different types of licenses:

Open Source Models

• What it means: The code and often the model weights are publicly available
• Examples: LLaMA, Mistral, BERT
• Pros: Free to use, can be modified, run locally
• Cons: Usually less powerful than commercial models, require technical knowledge

Closed Source Models

• What it means: The model is proprietary and only accessible through APIs
• Examples: GPT-4, Claude, Gemini
• Pros: More powerful, easier to use, better support
• Cons: Can be expensive, limited customization, dependency on the company

Hybrid Models

• What it means: Open source base with commercial add-ons
• Examples: Some versions of LLaMA, community fine-tuned models
• Pros: Balance of freedom and power
• Cons: Can be confusing to navigate

How to Choose the Right Model

Here's a simple decision tree:

1. What are you trying to do?

   • Text → Language Model
   • Images → Computer Vision Model
   • Both → Multimodal Model

2. How much control do you need?

   • Full control → Open Source
   • Ease of use → Closed Source
   • Middle ground → Hybrid

3. What's your budget?

   • Free → Open Source
   • Pay per use → Closed Source APIs
   • One-time cost → Self-hosted open source

Real-World Example

Let's say you want to create a chatbot for customer service:

• Closed Source Option: Use GPT-4 through OpenAI's API

  • Pros: Easy to implement, very capable
  • Cons: Costs money per conversation, limited customization

• Open Source Option: Use LLaMA 2 locally

  • Pros: Free, full control, can run offline
  • Cons: Requires technical setup, less powerful

What This Means for Prompt Engineering

Different models respond differently to the same prompt. A prompt that works perfectly with GPT-4 might fail completely with LLaMA, and vice versa.

Key Takeaway: Understanding your model's strengths and limitations is crucial for effective prompting.

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Next up: We'll dive into why prompting matters and how to make the most of whatever AI model you're working with.