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DevelopmentIntermediate59 lessons18–22 hours
Prompt Engineering Mastery
Write prompts that consistently produce usable output on the first try. Chain-of-Thought, ReAct, meta-prompting — the techniques that separate amateurs from professionals.
What's Included
- Personal AI coaching agent
- Lifetime access to content
- Student community access
- Completion certificate
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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 — the gap between mediocre and production-ready 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
- 1.6Lab: Rewrite 5 of your recent prompts using the framework and compare outputs
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
- 2.6Lab: Build a 5-example few-shot template for your most common task
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
- 3.6Lab: Solve 3 complex problems with and without CoT — measure the difference
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
- 4.6Lab: Apply ToT to a strategic decision and document your reasoning tree
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
- 5.6Lab: Build a ReAct research workflow for a topic you choose
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
- 6.6Lab: Generate and optimize 3 prompts using meta-prompting loops
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
- 7.6Lab: Extract structured data from 3 real documents and validate accuracy
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)
- 8.6Lab: Build a 20-prompt library for your specific domain
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
- 9.6Lab: Run a Reflexion loop on one of your worst-performing prompts
Module 10: Your Personal Prompt Engineering System (Capstone)
- 10.1Building your prompt library — organization, tagging, versioning
- 10.2Your Prompt Engineering Playbook — when to use which technique (decision flowchart)
- 10.3Teaching others — training your team on effective prompting
- 10.4Staying current — evaluating new techniques monthly
- 10.5Capstone: Assemble your complete prompt toolkit and test it on 5 real tasks
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