Two people ask the same AI the same question. One gets a vague paragraph they throw away; the other gets exactly the table they needed, in the format they wanted, with nothing invented. Same model, same day. The difference is entirely in how they asked — and that difference is a learnable skill with rules, patterns and failure modes. This free course teaches it the way it actually gets used at work: prompts as specifications you write, test, version and defend, not clever tricks you collect.
What is prompt engineering?
Prompt engineering is designing the input to a language model so it reliably produces the output you need. Not once, on your laptop — reliably, on inputs you have never seen, for every user.
That word reliably is where the engineering lives. Getting a good answer once is luck. Getting a good answer on the ninety-third messy real-world input is design: you specified the job, supplied the facts, showed the shape, and decided what should happen when the input is nothing like you expected.
💡 You may have read that "prompt engineer" is a dying job title. That's half true, and the half that matters is the other half: the standalone title is fading because the skill got absorbed into every AI role. Nobody advertises for a "SQL engineer" either — they just expect you to know SQL.
What a prompt is actually made of
A prompt looks like a paragraph of English, so it is easy to assume there's nothing to it. There is. Almost every good prompt fills five slots — and every slot you leave empty is a decision you handed to the model.
Lesson 2 takes each slot apart. For now, notice the honest implication: "inconsistent output" is usually an under-specified prompt, not an unreliable model.
Your first real prompt — with its output
Here is the same task written twice. Read the first one and predict what comes back.
prompt = "Look at this feedback and tell me about it: " + reviewResult
Fluent, polite, and completely useless if you have 4,000 reviews to process tonight. Now the specified version:
prompt = f"""Classify this customer review for an Indian e-commerce site.
Return JSON only, no prose:
{{"sentiment": "positive"|"negative"|"mixed",
"issues": [list from: delivery, quality, price, support, packaging],
"urgent": true|false}}
Rules:
- "urgent" is true only if the customer mentions a safety problem,
a wrong or missing item, or says they will escalate.
- Use only issues actually mentioned. Empty list is fine.
Review: {review}"""Result
Nothing clever happened. We named the job, listed the allowed values, fixed the output shape, and defined the one word that was ambiguous ("urgent"). The second output can be parsed, counted, charted and tested. The first can only be read.
Why learn this for placements
Because it now appears in interviews for roles that are not called "AI" anything. Service companies — TCS, Infosys, Wipro, Accenture, Cognizant, Capgemini — ask conceptual questions in the technical round: zero-shot versus few-shot, what chain-of-thought is and when it helps, what prompt injection is. Product companies — Amazon, Google, Microsoft, Adobe — push into design: how you would evaluate a prompt, budget a context window, or stop a poisoned document from steering an agent.
And it shows up in the work itself. If you are building anything on an LLM — a chatbot, a summariser, a RAG system, an agent — the prompt is the part you actually control. Model choice is a line in a config file; the prompt is your product.
If you are preparing for a specific company, the exam guides are worth pairing with this: TCS NQT, Cognizant and the rest.
How this course works
Ten lessons in learning order. Each one is a page you read like a chapter — casual explanation first, the technical term afterwards, diagrams where the idea is spatial, and a real before/after prompt wherever a rule is claimed.
Every lesson ends with a Practice Zone: six MCQs from real selection rounds (with the company logos on each question), plus two or three hands-on tasks where you write or fix a prompt and then compare against a worked solution. Attempt before revealing — recognising an answer and producing one are different skills, and only one of them gets you through an interview.
Course roadmap
| # | Lesson | What you walk away with |
|---|---|---|
| 1 | What Is Prompt Engineering? | why wording changes output at all, and what the job really is |
| 2 | Anatomy of a Prompt | role, instruction, context, examples, format — and what each fixes |
| 3 | Zero-shot, One-shot & Few-shot | when examples help, how many, and how to choose them |
| 4 | Chain-of-Thought Prompting | step-by-step reasoning, self-consistency, and its honest limits |
| 5 | Structured Output & JSON | getting parseable output every time, schemas, and validation |
| 6 | System Prompts & Personas | standing rules that hold, and what a persona can and can't do |
| 7 | Prompt Patterns | delimiters, decomposition, self-critique, instruction placement |
| 8 | Context Engineering | the 2026 keyword: budgeting everything the model sees |
| 9 | Prompt Injection & Safety | why it can't be "fixed", and the layers that contain it |
| 10 | Evaluation & Versioning | eval sets, LLM-as-judge, rollbacks — prompts treated as code |
Company-wise prompt engineering PYQs
After lesson 10 the sidebar continues into company pages — the questions actually reported from each company's rounds, with full answers: TCS, Infosys, Wipro, Accenture, Cognizant, Amazon, Google and Microsoft.
How to study this course
Read in order — lesson 8 assumes lesson 2, and lesson 9 assumes you know what a system prompt is. Keep a chat window open beside the page and actually run the before/after prompts; the gap between them is far more convincing when it happens on your screen.
Do the hands-on tasks by writing your own version first, then revealing. Every one of them is the shape of a real interview question — "here is a bad prompt, fix it" — and the only way to get fast at that is to have done it a dozen times.
🎯 If you have one evening: lessons 1, 3, 4 and 9 cover the four questions that come up most often — what prompt engineering is, zero-shot vs few-shot, chain-of-thought, and prompt injection.
FAQ
Do I need to know Python or machine learning first?
No. Every idea here works in a plain chat window. The few code examples are short API calls shown for context — you can read them without writing any, and nothing in the course requires you to train anything.
Isn't prompt engineering becoming useless as models improve?
Better models forgive sloppy wording, which retires the tricks. What they don't do is guess which of six categories you meant, which facts they're allowed to use, or what to return when the input is garbage. That specifying work is the part that has grown — it is why the field renamed itself context engineering (lesson 8) rather than disappearing.
How is this different from the GenAI course?
The Generative AI course explains how models work — tokens, transformers, temperature, hallucination. This one is about controlling them. They pair well; if you have never met the word "token", start there and come back.
Can I put prompt engineering on my resume?
As a skill inside a project, yes — and phrase it the way this course does: "built and evaluated prompts for a ticket classifier; 200-case eval set; 94% accuracy" is interesting. "Prompt engineering expert" on its own invites the one question you don't want, which is "how do you know it works?"
Ready? Start here — Lesson 1: What Is Prompt Engineering? →