Stanford AI Lecture: How to Build AI Agents, Prompt Engineering & RAG Systems @AICreatorForge

Description

This Stanford CS230 AI lecture by Kian Katanforoosh is a practical guide to building reliable AI agents, prompt engineering workflows, and Retrieval-Augmented Generation (RAG) systems for production. In this 2-hour Stanford AI lecture, you will learn how to move beyond simple prompting and design scalable AI applications using modern LLM engineering practices. The session covers key techniques such as chain-of-thought prompting, prompt chaining, Retrieval-Augmented Generation (RAG) to reduce hallucinations, when to use fine-tuning vs prompting, and how to design agentic AI systems with tools and memory. You’ll also learn why production AI systems require separating orchestration from execution, deterministic code for math and state handling, and strong evaluation frameworks to measure AI reliability. This lecture is essential for AI engineers, machine learning developers, prompt engineers, and researchers building real-world applications with LLMs, AI agents, and RAG pipelines. - Topics covered in this Stanford AI lecture: - AI agents and agentic workflows - Prompt engineering and prompt chaining - Chain-of-thought reasoning - Retrieval-Augmented Generation (RAG) architecture - Fine-tuning vs prompting strategies - Building reliable LLM systems - AI evaluation and benchmarking - Designing scalable AI applications If you're building AI products, LLM applications, autonomous AI agents, or RAG-based systems, this Stanford lecture provides valuable insights into real-world AI system design. #stanfordai #buildwithai #rag #aiagents