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Prompt Engineering: The Complete Course
Go from prompt novice to confident practitioner with hands-on training in prompt engineering. Learn how to communicate effectively with large language models, structure complex problems into solvable tasks, and build production-ready AI applications that solve real-world challenges.
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108 Lectures
Comprehensive Knowledge
16 Hours
Video Duration
30+ Labs
Focus on Practice
Course Certificate
Validate Your Learning
Course Contents
01
Introduction
This section covers the foundational introduction to prompt engineering, exploring why it matters as a critical skill in generative AI development, and introducing the course structure and project overview. It discusses how effective communication with AI systems requires clear, precise prompts that can be learned and mastered, along with practical considerations around AI toolbox organization, costs, and the overall course project design.
Introduction
Why Learn Prompt Engineering?
Aligning Expectations
Course Project: What We Will Build
Course Project: Module Breakdown
Cost Overview for Using the OpenAI API
Let's Stay Connected!
Course Resources
02
Tools and Local Setup
This section covers essential setup and configuration tasks needed to begin working with generative AI APIs, including managing local Python environments, creating accounts with major AI providers, generating and securely storing authentication credentials, and understanding the various provider options available for model interaction.
Section Introduction
Python Local Setup
OpenAI Setup
Anthropic Setup
IMPORTANT! Setting Up Python and the Local Environment
A Note on Using GitHub Copilot for Our Project
03
Crash Course: The OpenAI Python Library
This section provides a comprehensive exploration of working with the OpenAI API, including environment setup, authentication, making API calls, using a unified library for multiple providers, understanding response objects, controlling model behavior through parameters, implementing real-time response streaming, and running models locally for zero-cost interaction.
Section Introduction
Setting Up the Environment
Handling Authentication
Your First Chat Completion Call
Introduction to LiteLLM
Using LiteLLM with Anthropic
The Response Object
Useful Parameters: temperature and max_tokens
IMPORTANT: Adapting to max_completion_tokens for GPT-5 Models
Useful Parameters: stop, n, response_format
Streaming
Running LLMs Locally with Ollama
04
AI Toolbox #1 - Setup and First Comand
This section focuses on implementing the first module of the AI Toolbox project, which establishes a foundational Python package structure with a working CLI application that includes an AI-powered command. The implementation leverages generative AI tools and demonstrates project scaffolding, dependency management, and integration of API functionality.
Module 1: Overview
Scaffold the Project
Codebase Walkthrough
Implement a Minimal CLI with Click
AI-Powered Hello
Testing Our Feature
05
Foundations: How LLMs "Think"
This section explores the foundations of how large language models work internally, including tokenization, probability distributions, context windows, and cost implications. It covers comparing different model types by their capabilities and characteristics, and demonstrates how system prompts take priority in guiding model behavior throughout conversations.
Section Introduction
What Is Tokenization?
Lab: Tokenization - Part 1
Lab: Tokenization - Part 2
Understanding Log Probabilities
Lab: How LLMs Build Sentences
The Context Window
Usage Cost Main Components
Lab: Usage Cost - Part 1
Lab: Usage Cost - Part 2
Model Classes
Prompt Roles
Lab: System Prompts
06
Core Prompting Patterns for Developers
This section covers core prompting techniques that form the foundation of effective prompt engineering, including the three pillars of effective prompts, structural delimiters, persona patterns, few-shot prompting, and advanced techniques for formatting and reasoning.
Section Introduction
Instruction, Context, and Constraints
Lab: Instruction, Context, and Constraints
The Power of Delimiters: Delimiter Overview
The Power of Delimiters: Refactoring a Prompt
Lab: Using Delimiters
The Persona Pattern
Lab: The Persona Pattern
Lab: Defining Behavioral Rules
Lab: Example - Defining a DBA Expert
Few-Shot Prompting
Lab: Few-Shot Prompting
Techniques for Formatting Output - Part 1
Techniques for Formatting Output - Part 2
The Chain-of-Thought Pattern
Lab: Practicing Chain-of-Thought
The Template Pattern
Lab: Practicing the Template Pattern
07
AI Toolbox #2 - Smart Commits
This section covers implementing the second module of the AI Toolbox project, building a smart commit feature that generates conventional commit messages from code changes with an interactive user feedback loop.
Module 2: Overview
Scaffold the Commit Feature Boilerplate Code
Retrieve the Git Diff
Generate the Prompt Templates
Implement the Commit Functionality
Allow Commit Message Adjustments
Improve the Test Suite
Add Proper Logging to the Commit Feature
Allow Choosing a Custom Model via the CLI
Add Documentation
08
Advanced Prompting Techniques
This section covers sophisticated prompting techniques that enable better interaction with large language models through strategic prompt design patterns. Students learn how to restructure tasks, generate optimized prompts through AI assistance, break down complex problems, iteratively improve outputs, extend model capabilities through function calling, and synthesize multiple diverse solutions into superior final results. The patterns demonstrated form a toolkit for handling ambiguity, complexity, and the need for refined outputs across various application domains.
Section Introduction
Lab: Flip the Script Pattern - Part 1
Lab: Flip the Script Pattern - Part 2
Lab: Generate Prompts - Part 1
Lab: Generate Prompts - Part 2
Lab: The Decomposition Pattern
Lab: The Self-Critique Pattern
Lab: Implement Function Calling - Part 1
Lab: Implement Function Calling - Part 2
Lab: The Self-Consistency Pattern - Part 1
Lab: The Self-Consistency Pattern - Part 2
09
AI Toolbox #3 - Smart Code Reviews
This section guides students through building a sophisticated multi-step code review pipeline that orchestrates multiple AI personas, integrates external tools through function calling, and synthesizes diverse perspectives into comprehensive reports. The project demonstrates how to apply all the prompt engineering techniques from previous sections in an integrated, production-oriented system that uses decomposition, self-consistency, function calling, and iterative refinement to create intelligent analysis capabilities.
Module 3: Overview
Module TODOs Overview
Migrate from Subprocess to GitPython
Refactor Exceptions
Scaffolding the Review Command
Implementing Dataclasses and Helper Functions
Integrating the Review Command into the CLI
Implementing Logic and Syntax Review Prompts
Leveraging LLMs for Reviews
Creating Relevant Personas for More Advanced Analysis
Implementing the Self-Consistency Workflow - Part 1
Implementing the Self-Consistency Workflow - Part 2
Define Functions for Tool Calling
Implement a Lightweight Tool Registry - Part 1
Implement a Lightweight Tool Registry - Part 2
Fix Typing Issues
Implement a First Draft for AI-Driven Tool Calling
Improve the Tool Calling Loop
Implement Tests for Tool Calling
Add a Self-Critique Step in the Review
10
AI Toolbox #4 - JSON and Markdown Output
This section transforms the code review system from text-based to data-driven architecture, migrating toward structured data classes and JSON schemas throughout the pipeline. Students refactor the system into modular packages, update prompts to return structured JSON matching data class schemas, and implement flexible output formatting that supports both JSON and Markdown representations. This section demonstrates how thoughtful data architecture enables robust, maintainable systems that can adapt to new requirements and integrate with external tools more effectively.
Module 4: Overview
Start with Output Migration
Split the Review Module into Multiple Files
Fix Failing Tests
Migrate Existing Prompts to Output JSON
Update Existing Tests
Migrate Review Pipeline and Helpers to Work with Dataclasses - Part 1
Migrate Review Pipeline and Helpers to Work with Dataclasses - Part 2
Complete Pipeline Migration
Fix Minor Bugs
Fix Existing Tests
Implement Output Parsing for the Review Command
Project Wrap-Up: Update Synthesis Logic and Project Documentation
11
Conclusion
Congratulations!
Certificate of Completion
Frequently asked questions
Who is this course designed for?
This course is designed for multiple technical roles:
Software Engineers and Developers who want to integrate AI capabilities into their applications will gain the prompt engineering skills needed to build AI-powered features, automate code generation tasks, and create intelligent systems that solve real problems with reliable outputs.
Data Scientists and AI Engineers seeking to deepen their understanding of how to work effectively with language models will learn systematic approaches to prompting, evaluation, and iteration that transform raw model outputs into production-ready solutions.
DevOps Engineers and Technical Leads managing or deploying AI-powered tools will understand how to architect maintainable systems, manage costs, integrate external tools, and build applications that scale from prototype to production.
Entrepreneurs and Product Managers exploring AI-powered products will gain hands-on technical knowledge to understand what is possible, evaluate different approaches, and make informed decisions about AI technology selection for their projects.
What prior knowledge do I need before taking this course?
You should have familiarity with Python programming, as you will be writing code throughout the course to interact with AI APIs and build applications. Basic comfort with running commands in the terminal and using the command line is highly recommended, as you will be working extensively with Python scripts and command-line tools.
No prior knowledge of prompt engineering, large language models, or AI APIs is required. The course starts from the fundamentals and builds your knowledge progressively through hands-on exercises and real-world projects.
Will I incur costs (AI provider APIs) while taking this course?
The course is designed to minimize costs while providing hands-on experience with real AI APIs.
Free Components:
Does this course cover specific models like Claude, GPT-4, or Llama?
This course teaches prompt engineering principles that apply across all major language models. While examples use models from OpenAI and Anthropic, the techniques you learn transfer directly to any model including Claude, GPT-4, Llama, Mistral, and others.
The course demonstrates how to use unified provider libraries that support multiple models, allowing you to write prompt-engineering code once and run it against different models. This means you can apply what you learn to whatever models your organization or projects use.
Understanding the fundamental principles of effective prompting is more valuable than learning model-specific syntax, as it enables you to adapt quickly to new models as they are released and evolve.
Can I run language models locally instead of using cloud APIs?
Yes. The course demonstrates how to download and run large language models locally using tools like Ollama, giving you complete privacy, zero cost, offline capability, and full control over your interactions. The unified provider library used throughout the course supports local models alongside cloud APIs.
You can use local models for development, testing, and learning without incurring any API costs. Many students use local models for initial development and switch to cloud APIs when they need more capability or production deployment. The choice is yours, and the course teaches both approaches.