#11 · Primary category: Prompt Engineering Tools
Prompt_Engineering
22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.
Project last updated:08/28/26
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Why we included this project
Anyone starting to build with LLMs quickly learns that prompts decide how much you get out of the model, yet most people pick them up by trial and error. This collection walks through 22 prompt engineering techniques in Jupyter notebooks, so each idea comes with runnable code instead of just a description. It starts with the basics like structured templates and role prompting, then moves into the reasoning strategies that matter for harder problems: chain-of-thought, self-consistency, and tree-of-thought. That progression works both as a first tutorial and as a reference to revisit when you are deciding which technique fits the task at hand. The notebooks are learning material, not a deployable library, but the patterns translate directly into the prompts you would use in real applications.
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