In this episode of my AI Masterclass series, I break down the must-know types of open-source AI models in the simplest way possible. If you’re trying to build anything in AI—chatbots, search, RAG pipelines, or agentic systems—you need a clear mental model of what kind of model you’re actually using. So I explain the three core categories you’ll keep seeing everywhere: decoder-only, encoder-only, and encoder-decoder (hybrid) models. First, I cover decoder-based models, which are the classic text generators—token-by-token—like what you experience in ChatGPT-style assistants. These are strong for conversation and coding, and they’re the most popular model type today. Then I move to encoder-based models, which focus on understanding text instead of generating it. These are critical for embeddings, similarity search, classification, and fast retrieval workflows. Finally, I explain encoder-decoder models, the hybrid approach where you get both understanding and generation—great for translation and summarization—but they’re heavier, so running them locally usually needs a powerful machine. By the end, you’ll know which model family fits your project and why that choice matters when you’re building production-ready AI systems.