[Stanford CS146S #2] Inside Coding LLMs & Prompt Engineering: Pre-training, RLHF, and the 'Trust but Verify' Swiss Cheese Model
Welcome back to the technical deep-dive series covering Stanford University's CS146S: The Modern Software Developer.
In Lecture 1, we established the macro architectural perspective: how modern software engineers are pivoting from manual syntax typists into conductors presiding over autonomous agent squads. In Lecture 2 (Week 1), the curriculum dives directly into the underlying computational engine powering this revolution: Coding Large Language Models (LLMs) and the engineering discipline of Prompt Engineering.
Drawing directly from foundational works by Andrej Karpathy (Deep Dive into LLMs), OpenAI (How OpenAI Uses Codex), and Google Cloud/DAIR.AI prompt engineering research, this lecture demystifies how neural networks acquire coding fluency, why they remain statistical simulators rather than conscious reasoning entities, and how software engineers must implement the "Trust but Verify" doctrine to overcome the treacherous Swiss Cheese Model of automated code generation.