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Self-paced Intermediate

Prompt Engineering Basic to Advance

3(118)
511 views
Trainers Rating
3(118)
Course type
Watch to learn anytime
Duration
137 min
Start
Anytime
Language
English
Views
511
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  • Interactive Video Lessons
  • Learn from Industry Expert
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Why enroll

People enroll in the Prompt Engineering: Basic to Advanced course because it empowers them to communicate effectively with AI and get better, more accurate results from AI tools. As AI becomes an essential part of education, business, and everyday work, learners want practical skills that help them save time, boost creativity, and solve problems more efficiently. This course helps participants understand how AI responds to instructions, avoid common mistakes, and design clear, powerful prompts for real-world tasks such as content creation, research, coding, and productivity. By progressing from beginner to advanced techniques, learners gain confidence and a competitive edge, making this course valuable for students, professionals, educators, and anyone looking to use AI intelligently and responsibly.

Course details

This course provides a comprehensive introduction to Prompt Engineering, guiding learners from foundational concepts to advanced techniques for effectively interacting with AI language models. Students will learn how prompts work, how to structure clear and precise instructions, and how to refine prompts to achieve accurate, creative, and reliable outputs.

Starting with the basics, the course covers prompt types, context setting, role prompting, and common mistakes. As learners progress, they will explore advanced strategies such as multi-step reasoning, prompt chaining, few-shot learning, output control, and optimization techniques for complex tasks. Real-world use cases—including content creation, coding assistance, data analysis, education, and productivity—are integrated throughout the course.

Course suitable for

Is this course for you?

Perfect for

  • You work in Aerospace or Automotive
  • You're a Data Science & Analysis professional
  • You have some foundational knowledge in the subject
  • You prefer self-paced learning you can revisit

Before you enrol

  • Intermediate level — assumes the fundamentals of Data Science & Analysis
  • Self-paced video, 137 min — watch anytime
New to Data Science & Analysis? Browse beginner courses →

Ready for the next step?

Paid courses that build on what this one teaches.

Course curriculum

The course is readily available, allowing learners to start and complete it at their own pace.

10 lectures2 hr 17 min
  1. Introduction of Prompt Engineering
    14 min
  2. Types of Prompt
    10 min
  3. Anatomy of a Prompt
    8 min
  4. Prompt Anatomy
    13 min
  5. 4 Method of Prompt Engineering
    13 min
  6. Prompt Tuning
    9 min
  7. LLMs — How ChatGPT works & What is RAG?
    16 min
  8. Advanced Prompt Techniques
    22 min
  9. 99% of you prompt AI wrong
    24 min
  10. Context Engineering vs. Prompt Engineering
    8 min

Key topics covered

Introduction to AI & Prompt Engineering

Prompt Engineering Basics

Prompt Design Techniques

Intermediate Prompt Engineering

Opportunities that await you!

Career opportunities

Frequently asked questions

A: You need a defensible budget number to explain spend, and ~1.3 tokens/word gets you there with headroom. B doubles a typical English mapping without evidence. C assumes provider-side caching semantics that don't apply at billing time. D mixes up input/output accounting with legacy streaming myths.

A: You want a numeric feel for output variability, and scaling entropy by temperature gets you close enough for control discussions. B ignores that top_p truncates tails after temperature reshapes them. C compounds effects that don't multiply in practice. D reverses the direction of entropy change.

A: You're mapping safeguard to consequence, and reframing attacks bypass intent without violating literal constraints. B describes overblocking, not residual risk. C confuses safety controls with sampling controls. D assumes absolute priority without considering indirect elicitation.

A: You need repeatability against a known layout, and concrete examples anchor the model tightly. B trades control for flexibility you don't need. C adds verbosity and leakage risk without improving extraction. D increases variance against the stated constraint.

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What learners say about this course

Industry-aligned courses, expert training, hands-on learning, recognized certifications, and job opportunities-all in a flexible and supportive environment.

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