In this lecture, we explore the core techniques behind prompt engineering for large language models. We introduce zero-shot, few-shot, and many-shot prompting, explain when each approach is appropriate, and examine common failure modes such as ambiguity, format drift, and context window limits. Through practical examples, we show how prompt structure, examples, and constraints influence model behavior, setting the foundation for more advanced techniques covered later in the course.
