AI and LLM Fundamentals

Overview This lesson teaches learners how to assess, optimize, and refine prompts and outputs. You will learn strategies to measure output quality, troubleshoot issues, and iteratively improve LLM interactions for practical applications. Concept Explanation 1. Importance of Evaluation 2. Evaluation Methods a) Manual Review b) Automated Metrics c) Few-shot Testing…

Overview This lesson teaches learners how to apply advanced prompt engineering techniques to practical tasks such as code generation, debugging, translation, and multimodal interactions. We also introduce automatic prompt optimization, a technique to let LLMs help refine their own prompts. Concept Explanation 1. Automatic Prompt Engineering (APE) Example: 2. Code…

Overview In this lesson, learners will understand how large language models (LLMs) generate text, the concept of token prediction, and basic strategies to control outputs like temperature, top-K, and top-P sampling. This is foundational knowledge to prepare for effective prompt engineering. Concept Explanation 1. LLMs as Prediction Engines Key Idea:…

Overview In this lesson, learners will understand how large language models (LLMs) generate text, the concept of token prediction, and basic strategies to control outputs like temperature, top-K, and top-P sampling. This is foundational knowledge to prepare for effective prompt engineering. Concept Explanation 1. LLMs as Prediction Engines Key Idea:…

Overview In this lesson, you will learn: By the end, you will be able to use multiple AI tools effectively for productivity, creativity, and learning. Key Concepts Concept Explanation 1. ChatGPT / OpenAI 2. Claude (Anthropic) 3. Google Gemini 4. Dify / Notion AI Practical Examples Example 1 – ChatGPT…

Overview In this lesson, you will learn: By the end, you will understand the mechanics of LLMs and how they generate human-like text. Key Concepts Concept Explanation 1. Tokens 2. Transformers 3. Attention Mechanism 4. Next-Word Prediction Practical Examples Example 1 – Tokenization Example 2 – Next-Word Prediction Example 3…