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Prompt Engineering for AI: Master AI & LLMs
Rating: 4.5 out of 5(59 ratings)
623 students

Prompt Engineering for AI: Master AI & LLMs

Master Prompt Engineering, LLMs, Reasoning, Context Engineering, Structured Outputs, AI Agents, Evaluation & LLMOps
Created byPius Dave
Last updated 10/2026
English
English [Auto],

What you'll learn

  • Students who want to develop practical Generative AI and Prompt Engineering skills
  • Developers and software professionals who want to work with LLMs and AI-powered applications
  • Data scientists, analysts and ML professionals interested in Generative AI
  • AI/ML enthusiasts who want to move beyond basic ChatGPT usage
  • Business and technology professionals who want to use AI more effectively in their work
  • Entrepreneurs and professionals interested in building AI-powered workflows and applications
  • Professionals who want to understand reasoning models, context engineering, structured outputs and AI agents

Course content

2 sections • 82 lectures • 11h 26m total length
  • Course Introduction5:12
  • 500 Important Prompts1:54
  • Foundations of Prompt Engineering9:11
  • Thank You4:43
  • Prompting Techniques — Core Skills9:30
  • Reasoning Models & Thinking Controls8:30
  • Prompt Engineering Across Models8:26
  • Context Engineering8:13
  • Structured Output & Tool Use8:11
  • Agentic Prompting8:11
  • Applications & Use Cases8:00
  • Evaluation, Testing & Optimization8:06
  • Risks, Safety & Guardrails8:36

    Guard against prompt injection, jailbreaking, hallucination, bias, and privacy by building layered defenses from the start, separating trusted instructions from untrusted data, grounding answers, and red-teaming continuously.

  • Production & Ops — LLMOps6:44

    Master llm ops by designing, testing, and deploying prompts with version control, cost and latency management, and robust monitoring. Route tasks to the right models and guard against drift.

  • Prompt Engineering Rapid Learning19:15
  • Prompt Optimization Engineering59:54

Requirements

  • No prior Prompt Engineering or Generative AI experience is required. The course starts from the fundamentals and gradually progresses to advanced concepts.
  • Basic familiarity with using a computer, web browser, and AI tools such as ChatGPT or similar AI assistants is helpful but not mandatory.
  • You should have access to a computer or laptop with a stable internet connection to follow the practical demonstrations and exercises.
  • No advanced programming knowledge is required for the core Prompt Engineering modules. Basic Python knowledge is helpful for the optional Python and AI/data-related practice sessions.

Description

Unlock the power of Generative AI by learning how to communicate effectively with modern AI models.


This comprehensive course takes you beyond basic ChatGPT prompting and teaches you how to design, test, evaluate, and optimize prompts for real-world AI applications.


You will start with the foundations of Prompt Engineering and progressively move into advanced techniques including reasoning models, thinking controls, context engineering, structured outputs, tool use, agentic prompting, evaluation, optimization, safety, and LLMOps.

The course is designed to help you understand not only WHAT prompt to write, but WHY a prompt works, how to improve it, and how to build reliable AI workflows around Large Language Models (LLMs).

What you will learn

• Fundamentals of Prompt Engineering
• Core prompting techniques and best practices
• Designing effective instructions and prompts
• Reasoning models and thinking controls
• Prompt Engineering across different AI models
• Context Engineering for better AI responses
• Structured outputs and reliable AI responses
• Tool use and function-oriented prompting
• Agentic Prompting and AI agent workflows
• Practical AI applications and use cases
• Prompt evaluation, testing and optimization
• Risks, safety, limitations and AI guardrails
• Production considerations and LLMOps


The course also includes a bonus Python session covering Python fundamentals, NumPy, Pandas, SciPy, Matplotlib, Seaborn and Plotly to help learners who want to work more practically with AI and data.

Who is this course for?


This course is suitable for:

• Students who want to build practical Generative AI skills
• Developers and software professionals working with AI/LLMs
• Data science and analytics professionals
• AI/ML enthusiasts
• Business professionals looking to use AI more effectively
• Entrepreneurs and professionals building AI-powered solutions
• Anyone who wants to move beyond basic ChatGPT usage and understand professional Prompt Engineering


You do not need to be an AI researcher to get started. The course gradually progresses from fundamental concepts to advanced Prompt Engineering and AI application techniques.


What makes this course different?

Instead of treating prompting as simply "asking ChatGPT better questions", this course approaches Prompt Engineering as an engineering discipline.


You will learn how prompts interact with models, context, reasoning, tools, structured outputs, evaluation and AI agents.

By the end of the course, you will have a structured framework for designing better prompts and building more reliable AI-powered workflows.


Start your journey from basic prompting to advanced Prompt Engineering and learn how to work effectively with modern AI systems.


Who this course is for:

  • Students and beginners who want to learn Prompt Engineering and develop practical skills for working with Generative AI and Large Language Models.
  • Developers, software professionals, data professionals, and AI/ML enthusiasts who want to learn advanced prompting, reasoning models, context engineering, structured outputs, tool use, and agentic prompting.
  • Business and technology professionals who want to use AI more effectively for problem-solving, automation, content generation, analysis, and other real-world applications.
  • Anyone who wants to move beyond basic ChatGPT usage and learn how to design, evaluate, optimize, and build more reliable AI-powered workflows and applications.