Advanced Parameters Top-P Max Tokens Frequency Penalty Presence Penalty LLM Limitations 1. No True Understanding 2. Knowledge Gaps 3. Memory Constraints 4. Adaptability Issues Hallucinations AI can produce: Ethical Concerns Solving Limitations RAG (Retrieval-Augmented Generation) Human-in-the-Loop Fine-tuning & Retraining

What is a Large Language Model? A Large Language Model (LLM) is a type of AI model designed to understand and generate human language. At their core, LLMs are pattern-recognition systems, not thinking entities. Neural Networks – The Brain Behind AI LLMs are powered by Neural Networks: These networks learn by identifying patterns in data and forming connections. Machine Learning – How AI Learns Machine Learning allows systems to: Flow: Training Examples → Algorithm → Input Data → Prediction/Output During training, the model builds a mathematical representation of relationships within data. Transformers – The Key Breakthrough A Transformer is a special type of neural network designed for language. It works by: This is what makes LLMs powerful. Tokens – How AI Sees Language LLMs don’t see words—they see tokens. Token Types: Key Concepts: Vectors & Embeddings Tokens are converted into numbers: Each dimension represents: Training Process of LLMs 1. Pre-training 2. Fine-tuning How LLMs Generate Text Flow: Input → Tokens → Neural Network → Relationships → Predicted Token Interacting with LLMs 1. GUI (Graphical Interface) 2. API (Application Programming Interface) 3. CLI (Command Line Interface) AI Agents – Introduction AI Agents are systems that: They can: