Advanced Prompt Engineering
Go beyond basic prompting: master chain-of-thought, few-shot learning, system prompts, structured output, and prompt security. 8 lessons with certificate.
What You'll Learn
- Apply structured prompting techniques (XML tags, JSON schemas, COSTAR framework) to consistently produce high-quality AI output
- Use chain-of-thought, tree-of-thought, and self-consistency prompting to solve complex reasoning problems
- Design few-shot prompts with strategically chosen examples that teach AI your desired output pattern
- Build reusable system prompts that define AI behavior, constraints, and output formats for repeatable tasks
- Evaluate prompt security risks including injection attacks and implement defensive prompting patterns
- Create a personal prompt library with tested, versioned prompts for your most common AI workflows
Course Syllabus
Prerequisites
- Basic experience with AI assistants (ChatGPT, Claude, Gemini, or similar)
- Familiarity with writing simple prompts (you've used AI to generate text, answer questions, or complete tasks)
- No programming experience required (coding examples are optional extensions)
What You’ll Learn
You can write prompts that get decent results. But “decent” isn’t what you need when the AI is drafting a legal brief, analyzing financial data, or building a customer-facing product.
Advanced prompt engineering is the difference between “the AI kind of got it” and “the AI nailed it every time.” It’s the techniques that make outputs reliable, reproducible, and production-quality.
This course teaches the methods used by prompt engineers at companies building AI-powered products: structured prompting, reasoning chains, few-shot learning, system prompt design, output control, and security patterns. These aren’t tricks — they’re engineering practices that produce consistent results.
Who This Course Is For
- AI power users — you use AI daily and want more consistent, higher-quality results
- Developers — building AI-powered features or products and need reliable prompts
- Content professionals — writers, marketers, analysts who need AI output they can trust
- Prompt engineers — formalizing your skills with proven frameworks and patterns
- Anyone who’s hit a plateau — your basic prompts work but complex tasks produce inconsistent results
Course Structure
8 lessons, each 10-15 minutes. Each lesson teaches a technique with examples you can test immediately in any AI assistant. The capstone builds a personal prompt library you’ll use long after the course.
Frequently Asked Questions
How is this different from the basic Prompt Engineering course?
The basic course covers fundamentals: writing clear prompts, using context, and avoiding common mistakes. This course goes deeper: structured prompting with XML/JSON, chain-of-thought reasoning, few-shot learning, system prompt design, output control, and prompt security. If you can write decent prompts but want expert-level results, this is the next step.
Does this work with all AI models?
Yes. The techniques apply to Claude, ChatGPT, Gemini, Llama, Mistral, and other LLMs. We note model-specific differences where they matter — for example, Claude responds well to XML tags while GPT works well with JSON. The core principles are universal.
Do I need to know how to code?
No. All techniques are demonstrated in natural language. Some lessons include optional code examples for developers who want to use these techniques programmatically (via APIs), but the course is fully accessible without coding skills.
Will these techniques become obsolete as AI improves?
The specific syntax may evolve, but the principles are durable: structured communication, explicit reasoning, teaching by example, and security awareness. These mirror how humans communicate complex instructions to each other — they'll remain relevant as long as we interact with AI through language.