
Course Overview
This comprehensive course transforms you from a novice to an expert in Large Language Model (LLM) prompting. You’ll master the art and science of communicating with AI systems, learning evidence-based techniques that range from basic prompt construction to sophisticated reasoning frameworks. Through hands-on examples, real-world applications, and progressive skill-building, you’ll develop the ability to design, optimize, and deploy effective prompts for any task—from simple text classification to complex multi-step reasoning problems.20-25 Hours
Self-paced learning at your own speed
3 Modules
Comprehensive curriculum structure
Hands-On
Practical examples and exercises
What You’ll Learn
By completing this course, you will be able to:Design Effective Prompts
Create clear, effective prompts that consistently produce desired outputs from LLMs
Apply In-Context Learning
Master zero-shot, one-shot, and few-shot prompting techniques
Advanced Reasoning Methods
Implement Chain of Thought (CoT) and problem decomposition strategies
Optimize Systematically
Use self-refinement and ensembling techniques to improve prompt performance
Leverage External Tools
Integrate external knowledge through RAG (Retrieval-Augmented Generation)
Automate & Evaluate
Automate prompt design and measure effectiveness across diverse tasks
Prerequisites
No prior AI expertise required! This course is designed for learners at all levels.
- Basic familiarity with AI/LLMs (helpful but not required)
- Comfort with text-based interfaces
- Curiosity and willingness to experiment
Course Structure
Module 1: Foundations of Prompting
Duration: 3-4 hours Discover what prompting is, why it matters, and how it revolutionizes AI interaction. Build a solid foundation by understanding prompt mechanics, exploring basic templates, and learning core principles.Lesson 1.1: What is Prompting?
Lesson 1.1: What is Prompting?
Learn the prompt-response paradigm, understand how LLMs process inputs, and discover why prompting democratized AI access.
Lesson 1.2: Anatomy of a Prompt
Lesson 1.2: Anatomy of a Prompt
Master prompt components, template structures, formatting patterns, and role assignment techniques.
Lesson 1.3: In-Context Learning Fundamentals
Lesson 1.3: In-Context Learning Fundamentals
Explore zero-shot, one-shot, and few-shot learning modes with practical examples and pattern recognition.
Lesson 1.4: Core Prompting Principles
Lesson 1.4: Core Prompting Principles
Apply clarity, specificity, reasoning guidance, and formatting principles to create effective prompts.
Module 2: Task-Specific Prompting Patterns
Duration: 4-5 hours Apply fundamental concepts across different NLP tasks. Learn proven templates and techniques for classification, information extraction, generation, transformation, and question-answering.Lesson 2.1: Text Classification Prompts
Lesson 2.1: Text Classification Prompts
Design reliable classification prompts using constrained output, cloze-style completion, and explicit criteria.
Lesson 2.2: Information Extraction Prompts
Lesson 2.2: Information Extraction Prompts
Extract structured information from unstructured text through NER, relation extraction, and template-based patterns.
Lesson 2.3: Text Generation Prompts
Lesson 2.3: Text Generation Prompts
Generate creative and functional content with constraints, attributes, and multi-step approaches.
Lesson 2.4: Text Transformation Prompts
Lesson 2.4: Text Transformation Prompts
Transform text across languages, styles, formats, and reading levels while maintaining fidelity.
Lesson 2.5: Question-Answering Prompts
Lesson 2.5: Question-Answering Prompts
Build robust QA systems with structured reasoning, context handling, and uncertainty management.
Module 3: Advanced Prompting Techniques
Duration: 5-6 hours Elevate your expertise with sophisticated techniques that mirror advanced problem-solving strategies. Master Chain of Thought reasoning, problem decomposition, self-refinement, ensembling, and tool integration.Lesson 3.1: Chain of Thought (CoT) Prompting
Lesson 3.1: Chain of Thought (CoT) Prompting
Implement explicit reasoning steps that dramatically improve accuracy on complex problems.
Lesson 3.2: Problem Decomposition
Lesson 3.2: Problem Decomposition
Break down complex tasks into manageable sub-problems using least-to-most and recursive strategies.
Lesson 3.3: Self-Refinement & Iteration
Lesson 3.3: Self-Refinement & Iteration
Apply iterative improvement techniques and self-consistency methods for better outputs.
Lesson 3.4: Ensembling & Multi-Path Reasoning
Lesson 3.4: Ensembling & Multi-Path Reasoning
Combine multiple prompts and reasoning paths to improve robustness and accuracy.
Lesson 3.5: Tool Integration & RAG
Lesson 3.5: Tool Integration & RAG
Integrate external tools and knowledge sources to ground responses in factual information.
Learning Approach
Hands-On Examples
Work through real-world scenarios and practical applications
Progressive Building
Build skills incrementally from basics to advanced techniques
Evidence-Based
Learn techniques backed by research and proven in practice
Ready to Get Started?
Begin Module 1
Start with the foundations of prompting
Explore Resources
Access glossary, best practices, and further reading
Join Thousands of Learners Mastering AI CommunicationWhether you’re a developer, researcher, content creator, or business professional, this course will transform how you work with AI systems.