Prompt Engineering Mastery
For engineers who build features on top of large language models and want to pass the prompt engineering round of an AI interview. You will be able to explain zero-shot and few-shot prompting, sampling settings, reasoning and agent techniques, structured output and safety controls, and say which classic techniques still matter on 2026 reasoning models.
Course Content
Prepares you to define prompt engineering, compare zero-shot, few-shot, system and role prompts, list the practices that make prompts reliable, and explain how you prove a prompt works.
Prepares you to explain temperature, top-k and top-p, fix repetition loops and hallucinations, and split long tasks into steps, including which sampling settings current reasoning models no longer accept.
Prepares you to explain chain of thought, self-consistency, tree of thoughts and ReAct, how they differ, and how built-in thinking in 2026 reasoning models changed when each one is worth using.
Prepares you to explain the ReAct loop and how native tool calling implements it today, how automatic prompt optimisation works and how to pick a winning prompt fairly, how to template prompts safely, and how to design a support chatbot.
Prepares you to explain how to get reliable JSON and HTML from a model, how to prompt for code generation and debugging, how to prompt with images and documents, and how to extract and evaluate information at scale.
Prepares you to explain how prompts reduce bias and unsafe output, control writing style and inclusive language, use constraints and system-level instructions in sensitive tasks, and where prompts stop and code-level safeguards must take over.