Design systematic prompts, master LLM patterns, reduce hallucinations, and build reusable AI workflows
Most prompt engineering courses teach phrases that “always work.” This course takes a different approach.
What you’ll learn
- Understand how Large Language Models (LLMs) generate responses and why outputs vary.
- Design structured, multi-step prompts instead of one-off requests.
- Apply professional prompt patterns (Persona, Question Refinement, Fact Check List, etc.).
- Combine Zero-shot, Few-shot, Chain-of-Thought, and ReAct techniques for complex tasks.
- Reduce hallucinations and improve output accuracy using validation strategies.
- Build reusable, scalable prompt systems for professional workflows.
Course Content
- Introduction to the Course –> 5 lectures • 13min.
- How Neural Networks Work: What’s Inside? –> 11 lectures • 31min.
- Professional Prompt Patterns and Design Strategies –> 19 lectures • 49min.
- Systematic Methods in Prompt Engineering –> 6 lectures • 19min.
- Final Module: Key Insights and Next Steps –> 1 lecture • 2min.
Requirements
Most prompt engineering courses teach phrases that “always work.” This course takes a different approach.
Instead of memorizing prompts, you will learn how to design them as structured systems.
Prompt engineering is not about guessing wording. It is about understanding how Large Language Models (LLMs) process instructions, structure reasoning, and generate outputs. Once you understand the architecture, you can control the system more consistently and get more reliable results.
In this course, you will learn how to:
- Design multi-step prompts
- Apply professional prompt patterns such as Persona, Question Refinement, and Fact Check List
- Combine Zero-shot, Few-shot, Chain-of-Thought, and ReAct techniques for complex tasks
- Reduce hallucinations and improve reliability
- Validate and audit AI outputs using structured checks
- Build reusable and scalable prompt frameworks you can apply repeatedly
The course is practice-oriented. Each pattern is explained clearly and applied to realistic professional scenarios, so you can immediately transfer the techniques into your own work.
This course is especially valuable for analysts, legal professionals, researchers, developers, and strategic marketers who work with complex information and need structured AI outputs. If you want to manage an LLM as a tool — rather than rely on trial and error — you will benefit from this systematic approach.
By the end of the course, you will be able to design reproducible, testable, and scalable prompt systems for professional and analytical tasks. This is a systematic, architecture-focused approach to prompt engineering.