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DevelopmentIntermediate50 lessons18–22 hours
Prompt Engineering Mastery
Master the art and science of prompt engineering. Get consistently better outputs from any AI model.
What You'll Learn
Understand how LLMs actually work (tokens, attention, prediction)
Apply advanced techniques: Chain-of-Thought, Tree of Thoughts, Self-Consistency
Use the ReAct framework for research, tool use, and troubleshooting
Generate better prompts using meta-prompting and auto-optimization
Extract structured data (JSON, XML, tables) from unstructured text
Write domain-specific prompts for writing, coding, analysis, and creative work
Apply Reflexion and self-critique for higher quality outputs
Build a personal prompt library with 200+ tested prompts
Outcomes
- Write expert-level prompts using CoT, ToT, ReAct, and meta-prompting
- Extract structured data from unstructured text reliably
- Build a personal prompt engineering system you can use daily
- Debug and optimize prompts that aren't producing good results
Prerequisites
- -Experience using at least one AI model (ChatGPT, Claude, etc.)
- -No coding required for most modules
Projects You'll Build
- Build a domain-specific prompt library
- Create a structured data extraction pipeline
- Design your personal prompt engineering workflow
Course Curriculum
Module 1: The Science of Prompting
- 1.1How LLMs actually work — tokens, attention, prediction
- 1.2Why prompt engineering matters — 60% vs 95% useful output
- 1.3The Prompt Framework: Role + Context + Task + Format + Constraints + Examples
- 1.4Model differences — optimizing for ChatGPT, Claude, Gemini, Llama
- 1.5Setting up your prompt testing environment
Module 2: Zero-Shot and Few-Shot Prompting
- 2.1Zero-shot prompting — great results without examples
- 2.2Few-shot prompting — teaching by example
- 2.3Selecting effective examples
- 2.4Negative examples — showing what NOT to do
- 2.5Dynamic few-shot selection — choosing examples based on input
Module 3: Chain-of-Thought (CoT) Prompting
- 3.1What is chain-of-thought and why it improves reasoning
- 3.2Zero-shot CoT ("Let's think step by step")
- 3.3Manual CoT — providing reasoning examples
- 3.4When CoT helps and when it hurts
- 3.5CoT for different domains: math, logic, analysis, coding
Module 4: Tree of Thoughts (ToT) & Self-Consistency
- 4.1Tree of Thoughts — exploring multiple reasoning paths
- 4.2When ToT beats CoT
- 4.3Self-consistency — sampling multiple responses for accuracy
- 4.4Implementing self-consistency without API access
- 4.5Combining ToT and self-consistency for maximum accuracy
Module 5: ReAct (Reasoning + Acting)
- 5.1The ReAct framework — think, act, observe in a loop
- 5.2ReAct for research tasks
- 5.3ReAct for tool use
- 5.4ReAct for troubleshooting and debugging
- 5.5Building ReAct patterns for your workflows
Module 6: Meta-Prompting & Prompt Generation
- 6.1Meta-prompting — using AI to generate better prompts
- 6.2The prompt refinement loop: generate, test, critique, improve
- 6.3Automated prompt optimization
- 6.4Template engines — building reusable prompt templates
- 6.5Prompt portfolios — organizing and versioning your best prompts
Module 7: Structured Output & Data Extraction
- 7.1JSON mode and structured output
- 7.2XML tag patterns (Claude's preferred format)
- 7.3Table extraction and data parsing from unstructured text
- 7.4Classification and categorization prompts
- 7.5Entity extraction and named entity recognition
Module 8: Domain-Specific Prompt Engineering
- 8.1Prompts for writing (blogs, emails, reports, creative fiction)
- 8.2Prompts for coding (debugging, generation, review, refactoring)
- 8.3Prompts for analysis (data, market research, financial modeling)
- 8.4Prompts for creative work (brainstorming, design briefs, campaigns)
- 8.5Prompts for education (lesson plans, quizzes, tutoring)
Module 9: Reflexion, Self-Critique & Output Quality
- 9.1Reflexion — AI agents that learn from mistakes
- 9.2Self-critique prompts — "What's wrong with this response?"
- 9.3Iterative refinement — generate, critique, improve loops
- 9.4Quality scoring rubrics for AI output
- 9.5Hallucination detection and mitigation techniques
Module 10: Your Personal Prompt Engineering System (Capstone)
- 10.1Building your prompt library — organization, tagging, versioning
- 10.2The prompt engineering workflow — when to use which technique
- 10.3Teaching others — training your team on effective prompting
- 10.4Staying current — evaluating new techniques monthly
- 10.5The future of prompting — what changes as models get smarter
AI isn't slowing down.
Neither should you.
Every week you wait, the gap widens. The people who invest in learning AI now will be the ones leading teams, building companies, and staying ahead of the curve. This is your moment — don't let it pass.