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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

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What you are going to learn

Transform Your Skills with Practical AI Communication and Application Development

This course takes you from understanding how large language models work to building production-ready AI-powered applications. You will start with the essentials: API setup, tokenization, cost management, and the core principles of effective prompting. From there, you will develop expertise in techniques like few-shot prompting, chain-of-thought reasoning, persona patterns, and output formatting that give you reliable control over model behavior.

By the end of this course, you will be able to design multi-step AI pipelines using patterns like decomposition, self-critique, and self-consistency, implement function calling to connect models with external tools, and build complete AI features that are maintainable and testable. You will leave with both the prompting skills and the engineering mindset needed to ship AI-powered features in real applications.

By completing this course, you will be able to:

  • Set up Python development environments and securely manage API credentials for OpenAI, Anthropic, and other providers
  • Make API calls using unified provider libraries
  • Explain how tokenization works and calculate actual costs based on token usage and pricing models
  • Implement the three pillars of effective prompts
  • Use delimiters and structural formatting to organize prompts
  • Apply persona patterns to shape model outputs
  • Implement few-shot prompting
  • Design and execute chain-of-thought prompts
  • Build reusable prompt templates
  • Apply advanced patterns including flip-the-script, decomposition, and self-critique for sophisticated problem solving
  • Implement function calling and tool use
  • And much more!

Course Contents

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:
  • Python, Git, and all development tools are completely free
  • OpenAI provides free trial credits for new accounts
  • Anthropic provides free trial credits and ongoing free tier options
  • All course materials and resources are included

Optional Costs:
Most exercises can be completed using free trial credits from OpenAI and Anthropic. The course demonstrates actual costs transparently: planning and recording all examples cost less than 20 cents. If you choose to continue using APIs after free credits are exhausted, costs are typically minimal (usually under $5) unless you scale to production usage.

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.