Free Prompt Engineering Course | ChatGPT, Claude AI

Learn to communicate with AI models — craft prompts that get precise results. Master ChatGPT, Claude, and LLM applications.

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TL;DR: Learn to communicate with AI models — craft prompts that get precise results. Master ChatGPT, Claude, and LLM applications.

Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated Sep 1, 2026

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Free Prompt Engineering course: 38 lessons, from the basics through to the advanced topics interviewers actually ask about. Every lesson is free, needs no sign-up, and runs in your browser with an editor and instant AI feedback.

Prompt Engineering Course Syllabus — 38 Lessons

Work through them in order, or jump to the topic you need. 38 lessons include worked explanations and runnable examples.

Prompt Basics

What is Prompt Engineering — Prompt Engineering is the art and science of crafting effective instructions for AI models like ChatGPT, Claude, and others.
Zero-shot Prompts — A zero-shot prompt is when you ask an AI to do something without providing any examples.
One-shot Prompts — A one-shot prompt provides one example of what you want.
Few-shot Prompts — Few-shot prompting provides multiple examples (usually 2-5) to show the AI a pattern.
Role Prompting — Role prompting means asking the AI to adopt a specific role or persona before answering your question.
Context Setting — Context setting means providing background information that helps the AI understand the situation better.
Clear Instructions — Clear instructions are the foundation of good prompting.
Temperature & Creativity — Temperature is a parameter in AI models that controls how creative or predictable the response is.

Prompt Patterns

Chain-of-thought Prompting — Chain-of-thought prompting is a technique where you ask the AI to show its thinking step-by-step before reaching a conclusion.
Tree of Thought — Tree of thought is a more advanced pattern where the AI explores multiple possible approaches or solutions to a problem, like branches on a tree.
ReAct Pattern — ReAct stands for Reasoning and Acting. This pattern makes the AI explain its reasoning (why it's doing something), then take action (do it), then observe the result, and adjust if needed.
Self-consistency — Self-consistency means asking the AI to solve a problem multiple ways and pick the most consistent answer.
Step-by-step Reasoning — Step-by-step reasoning is the practice of breaking down complex ideas into smaller, sequential steps.
Output Formatting — Output formatting means specifying exactly how you want the AI's response organized.
Constraints in Prompts — Constraints are rules or limitations you give to the AI.
Persona Prompts — Persona prompts assign the AI a specific character or identity with particular knowledge, experience, and perspective.

Prompts for Productivity

Prompts for Summarizing — Summarizing is one of the most practical uses of AI in education.
Prompts for Writing — Writing prompts help you create essays, assignments, creative pieces, and structured documents.
Prompts for Coding — For students learning programming (Python, JavaScript, Java), coding prompts help you write, debug, and understand code.
Prompts for Research — Research prompts help you gather information, understand complex topics, and develop arguments for projects and papers.
Prompts for Teaching — Teaching prompts help educators and students create effective teaching materials: lesson plans, explanations, examples, quizzes, and activities.
Prompts for Analysis — Analysis prompts help you break down complex information, examine causes and effects, evaluate arguments, and draw conclusions.
Prompts for Brainstorming — Brainstorming prompts help you generate ideas, explore possibilities, and think creatively about problems.

Advanced Techniques

Prompt Chaining — Prompt chaining means breaking a large task into smaller prompts and feeding the output of one prompt into the next prompt.
System vs User Prompts — In advanced AI interactions, there are two types of messages: system prompts and user prompts.
RAG (Retrieval Augmented Generation) Concept — RAG (Retrieval Augmented Generation) is a technique where an AI system retrieves information from external sources (documents, databases, websites) before generating a response.
Prompt Injection Risks — Prompt injection is when someone embeds hidden instructions within content to manipulate the AI's behavior.
Avoiding Hallucinations — Hallucinations are when AI generates false or made-up information while sounding confident and credible.
Iterative Refinement — Iterative refinement means improving your prompt and response through multiple rounds.
Prompt Templates — Prompt templates are reusable formats you can fill in for repeated tasks.
Evaluation of Prompts — Evaluating prompts means assessing how well a prompt works.

Real-world Applications

Prompts for Students — Prompts for students cover the everyday use cases Indian students face: homework help, concept clarification, exam preparation, assignment writing, project guidance, and study planning.
Prompts for Education — Education-focused prompts are for teachers, educators, and anyone involved in teaching.
Using AI APIs (OpenAI/Claude) — For advanced users and developers, AI APIs (like OpenAI's API or Anthropic's Claude API) allow you to programmatically send prompts and receive responses.
Building a Chatbot — Building a chatbot means creating an application where users can chat with an AI.
Prompt for Data Extraction — Data extraction means using AI to extract structured information from unstructured text.
AI in Indian Schools — AI is transforming Indian education. From adaptive learning platforms (like BYJU'S, Vedantu) to AI tutors and assessment systems, Indian schools are adopting AI.
Career in AI Prompting — Prompt Engineering is becoming a career field.

Who this Prompt Engineering course is for

School and college students in India starting from zero, and anyone revising Prompt Engineering for placements, board practicals or a project. There is no prerequisite beyond being able to type — the first lessons assume no programming background at all.

How to study this course

Read the lesson, then run the example in the built-in editor before moving on — reading code is not the same as writing it.
Try the practice task at the end of each lesson. Getting it wrong and fixing it is where the learning happens.
When you are stuck, ask the free AI Tutor to explain that specific line rather than skipping ahead.

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