Prompt Engineering Techniques is a collection of hands-on Jupyter Notebook tutorials covering prompt engineering methods for working with large language models.
The repository addresses the challenge of learning and implementing effective prompting strategies by providing practical tutorials that progress from foundational concepts to advanced techniques. It covers approaches including chain-of-thought prompting, self-consistency, tree-of-thought prompting, few-shot learning, and in-context learning. Each technique is presented with executable notebook examples that demonstrate how to apply these methods when working with models like GPT, Claude, and other LLMs.
Developers learning prompt engineering or building applications with language models will find this useful as a structured learning resource. The material spans basic prompt templates through sophisticated multi-step reasoning strategies, making it suitable for both newcomers to the field and those seeking to deepen their understanding of advanced techniques. The repository complements a broader course offering that covers building production systems with AI, though the notebooks themselves stand alone as learning materials.
The project maintains an active collection of tutorial content with regular updates to reflect developments in prompt engineering practices and new techniques for leveraging language models.
