AI and LLM Fundamentals

Overview This lesson teaches learners how to mitigate risks in LLM outputs, align model behavior with user intentions, and reduce hallucinations or incorrect information. These practices are essential for building reliable, ethical, and user-safe AI systems. Concept Explanation 1. What Are Hallucinations? Example:Prompt: “List the top AI startups founded in…

Overview This lesson teaches learners how to design interactive AI systems capable of multi-step reasoning, memory of context, and human-like conversation. You will learn the principles behind chat workflows, reasoning chains, and task-oriented conversational agents. Concept Explanation 1. Multi-Step Reasoning 2. Conversational Agents 3. Context for Task-Based Interactions 4. Tool-Augmented…

Overview In this lesson, learners will understand how to expand LLM capabilities by integrating external knowledge. You will learn RAG (Retrieval-Augmented Generation), dynamic context management, and strategies for keeping outputs relevant and grounded. Concept Explanation 1. What is RAG? Key Idea: RAG combines retrieval (search) with generation (LLM output). 2.…

Overview This lesson teaches learners how to systematically assess LLM outputs, identify errors, debug issues, and iteratively improve prompts and workflows. Evaluation is critical for producing consistent, accurate, and trustworthy AI outputs. Concept Explanation 1. Why Evaluation Matters 2. Levels of Evaluation a) Prompt-Level Evaluation b) Workflow-Level Evaluation c) Quantitative…

Overview In this lesson, learners will explore how to structure and assemble LLM-based applications. You will learn how to translate user problems into prompts, manage context, design multi-step workflows, and evaluate outputs within a practical application framework. Concept Explanation 1. The Anatomy of an LLM Application 2. Converting User Problems…

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…