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Self-paced Intermediate

Prompt Engineering Basic to Advance

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FREE

137 min

Anytime

English

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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.

Your instructor

Engineering Academy

Engineer

Learn Without Limits: Free Engineering Courses

5+ yrs experience·India

Is this course for you?

You should take this if

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

You should skip if

You're looking for an introductory overview course
You need a different specialisation outside Data Science & Analysis
You need live interaction with an instructor

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

Key topics covered

Introduction to AI & Prompt Engineering

Prompt Engineering Basics

Prompt Design Techniques

Intermediate Prompt Engineering

Course content

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

10 lectures2 hr 17 min

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Questions and Answers

Q: You're estimating token usage because cost spiked overnight, and you type "estimate token count from prompt length without tokenizer" into Google. A system prompt is ~900 English words, user prompt ~300 words, output capped at 500 tokens. Order-of-magnitude, what's the total tokens per call?

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.

Q: Your lead asks you to cap randomness, and you search "how temperature and top_p interact in LLM sampling". Temperature is set to 0.8 and top_p to 0.9. For a next-token distribution with entropy H≈3.0 bits at temp=1.0, what's the adjusted entropy you should expect?

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.

Q: A jailbreak slips through, and you're googling "what does system prompt not protect against". The system prompt explicitly forbids legal advice. What's the residual risk that remains even when the system prompt is correctly applied?

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.

Q: You're designing a reusable prompt template and search "few-shot vs zero-shot prompt selection guide". The task is structured data extraction from invoices with stable format and low tolerance for drift. What prompt strategy fits best?

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.