Class 1: Introduction to AI and Large Language Models (LLMs) Learning Objectives Understand the foundational principles of Artificial Intelligence (AI) and how LLMs function. Develop skills in crafting effective prompts to interact with LLMs. Gain familiarity with real-world AI applications and their impact. Topics Covered What is Artificial Intelligence (AI)? Definition: AI is the field of computer science focused on creating systems that perform tasks requiring human intelligence, such as learning, reasoning, and problem-solving (IBM AI Overview). Brief history: From early AI in the 1950s to modern advancements like Deep Blue and ChatGPT. Examples: Virtual assistants (Siri, Alexa), recommendation systems (Netflix, Spotify). Introduction to Machine Learning (ML) Definition: ML is a subset of AI that enables computers to learn from data without explicit programming (MIT Sloan ML Explained). Types: Supervised, unsupervised, and reinforcement learning. Role in LLMs: ML algorithms power the training of LLMs on vast datasets. What are Large Language Models (LLMs)? Definition: LLMs are AI systems that understand and generate human-like text by analyzing massive text datasets (AWS LLM Guide). Examples: GPT-4 (OpenAI), BERT (Google), Claude (Anthropic). Applications: Chatbots, content generation, translation. How Do LLMs Work? Basic architecture: Transformers with attention mechanisms to understand context. Training process: Unsupervised learning on large datasets (e.g., Common Crawl, Wikipedia). Output generation: Predicting the next token based on input prompts. Basics of Prompting Definition: Prompting involves providing instructions or questions to guide LLM outputs (Prompt Engineering Guide). Types: Zero-shot (no examples), few-shot (few examples), chain-of-thought (step-by-step reasoning). Best practices: Be clear, specific, and provide context; use delimiters (e.g., triple backticks). Key Concepts Artificial Intelligence (AI) Machine Learning (ML) Natural Language Processing (NLP) Large Language Models (LLMs) Prompt Engineering Zero-shot, Few-shot, Chain-of-Thought Prompting Practical Exercises Explore AI Applications: Interact with a chatbot (e.g., ChatGPT or Grok) to understand its capabilities. Write and Test Prompts: Create prompts like “Explain quantum computing in simple terms” and test on a public LLM. Improve Prompts: Analyze responses and refine prompts for better clarity and accuracy. Resources Online tutorials: Coursera AI for Everyone LLM documentation: OpenAI API Prompt engineering guides: Medium Prompting Guide Delivery Notes