Lilly Tech Systems
AI Model Types
Master every category of AI model — from Large Language Models and embedding models to vision, speech, generative, and reinforcement learning systems. Understand what each model type does, when to use it, and how to choose the right one for your project.
Your Learning Path
Follow these lessons in order for a complete understanding of the AI model landscape, or jump to any topic that interests you.
1. Introduction
The AI model landscape in 2025, why understanding model types matters, taxonomy overview, and how different model categories relate to each other.
2. Large Language Models
GPT-4, Claude 4, Gemini, LLaMA 3, Mistral — architectures, parameter scales, capabilities, limitations, and when to use LLMs.
3. Embedding Models
Vector representations of text, images, and data. Semantic search, RAG, clustering, and similarity with models like text-embedding-3, Cohere Embed, and BGE.
4. Vision Models
Image classification, object detection, segmentation, and visual understanding with CNNs, Vision Transformers, YOLO, SAM, and GPT-4V.
5. Speech Models
Speech-to-text (Whisper, Deepgram), text-to-speech (ElevenLabs, OpenAI TTS), voice cloning, and real-time audio processing.
6. Classification Models
Sentiment analysis, spam detection, intent classification, and document categorization using BERT, DistilBERT, and specialized classifiers.
7. Recommendation Models
Collaborative filtering, content-based recommendations, hybrid systems, and modern deep learning approaches powering Netflix, Spotify, and Amazon.
8. Traditional ML Models
Decision trees, random forests, SVMs, linear regression, gradient boosting (XGBoost, LightGBM) — still essential for tabular data and production systems.
9. Fine-tuned Models
LoRA, QLoRA, full fine-tuning, instruction tuning, RLHF, and DPO. When and how to adapt pre-trained models for your specific domain.
10. Multimodal Models
Models that process text, images, audio, and video together. GPT-4o, Gemini, Claude 4 Vision, and the convergence of modalities.
11. Generative Models
Image generation (DALL-E 3, Midjourney, Stable Diffusion), video generation (Sora, Runway), music generation, and 3D model creation.
12. Reinforcement Learning
Q-learning, policy gradients, PPO, RLHF, game-playing agents, robotics, and how RL shapes modern AI alignment.
13. Choosing the Right Model
Decision frameworks, cost-performance tradeoffs, latency requirements, deployment constraints, and a practical flowchart for model selection.
What You'll Learn
By the end of this course, you'll be able to:
Identify Model Types
Recognize and distinguish between LLMs, embedding models, vision models, speech models, generative models, and more — understanding what makes each unique.
Match Models to Problems
Given any AI task, determine which model type is the best fit based on input/output requirements, latency, accuracy, and cost constraints.
Compare Leading Models
Evaluate and compare specific models within each category — GPT-4 vs Claude vs Gemini, YOLO vs SAM, Whisper vs Deepgram, and more.
Build AI Architectures
Design multi-model systems that combine different model types — such as using embeddings for retrieval and LLMs for generation in a RAG pipeline.