Prompt Engineering helps you guide AI models with clear and useful inputs. LLMs can write, plan, explain, and code. But their output depends on the words you give them. Many users test prompts without knowing how the model reads or shapes the reply. This course helps you build a clear and steady way to write prompts.
What You Learn
This course takes about two hours. You follow short steps and see simple examples. You learn why a prompt works and how to adjust it when it does not.
- See how LLMs read text through tokens, context size, and limits.
- Use a clear prompt structure with task, details, tone, output format, and needed context.
- Know when to use classic models and when to pick a reasoning model.
- Cut down model mistakes and keep replies steady with tested methods.
- Compare real prompt examples across different models.
Course Style
The course is short and hands‑on. You get prompt templates, notes on examples, and real use cases. You apply them to tasks like resumes, structured data, and product features.
Your Instructor
Nick is a senior QA engineer and technical project manager. He worked on Alexa at Amazon and has taught many students. He also guides teams on how to use AI in daily work, from APIs to internal tools.
