AI EngineeringBeginner

Prompt Engineering

From zero-shot basics to agentic AI systems — learn to communicate precisely with large language models.

10 modules60 lessons10 mock tests

Who it is for: Developers, AI practitioners and anyone building with LLMs.

What you will learn

  • Explain how LLMs process prompts and why they fail
  • Write structured prompts with roles, constraints and output formats
  • Use zero-shot, few-shot, chain-of-thought and ReAct techniques
  • Reduce hallucinations and ground answers with RAG and tools
  • Prompt agentic AI systems and apply GenAI design patterns
  • Ship production-ready prompts with testing and iteration

Syllabus

Mock Tests

Module 1 — 6 Topics

🎯
Topic 1

How Large Language Models Work

Understand the transformer architecture, tokenization, and how LLMs generate text — the foundation for effective prompting.

🧭
Topic 2

Tokens, Context Windows & Cost

Learn how tokenization works, what context windows mean, and how to manage costs effectively.

🔍
Topic 3

The Model Landscape: GPT, Gemini, Claude & Open Source

Navigate the rapidly evolving landscape of foundation models and understand when to use which.

💡
Topic 4

Temperature, Sampling & Generation Parameters

Master the parameters that control how LLMs generate text — temperature, top-p, top-k, and more.

📦
Topic 5

How Prompts Interact with Models

Understand the mechanics of how your prompt text influences model behavior at a technical level.

⚖️
Topic 6

Evaluating AI Outputs: Metrics & Judgment

Learn how to objectively measure and evaluate the quality of LLM-generated outputs.

Mock Tests

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Module 1

PE Module 1 Mock Test: How AI Actually Thinks

14 questions · 17 min

Frequently asked questions

Course content is produced with AI assistance.