<h2>About this course</h2>
<p>This official OpenAI guide teaches you how to write prompts that get better results from GPT models. Whether you're using ChatGPT, GPT-4, or building with the OpenAI API, <a href="/topics/prompt-engineering">prompt engineering</a> is the practical skill that separates good outputs from great ones. Learn directly from OpenAI's team how to structure requests, guide model behavior, and unlock the full potential of <a href="/topics/llm">large language models</a>.</p>

<h2>What you'll learn</h2>
<ul>
  <li>Six core prompt engineering strategies to consistently improve model responses</li>
  <li>How to write clear, specific instructions that GPT models understand and follow</li>
  <li>Techniques for breaking complex tasks into simpler sub-tasks for better accuracy</li>
  <li>How to give models time to reason through problems step-by-step before answering</li>
  <li>Ways to guide models to explain their thinking and reduce hallucinations</li>
  <li>Practical patterns for refining prompts based on model output</li>
  <li>Real-world examples you can adapt for your own projects and workflows</li>
</ul>

<h2>Who this is for</h2>
<p>You're a beginner curious about AI and ready to learn from the source. This course doesn't assume you've used GPT models before—just that you're willing to experiment and learn by doing.</p>
<ul>
  <li><strong><a href="/for/students">Students</a></strong> — build hands-on AI skills that employers value, especially as companies adopt GPT tools across roles</li>
  <li><strong>Professionals switching to AI roles</strong> — gain practical prompt engineering knowledge to support your transition into data, product, or AI-focused positions</li>
  <li><strong>Content creators and <a href="/for/marketers">marketers</a></strong> — master prompts to generate ideas, refine copy, and automate routine writing tasks</li>
</ul>

<h2>Prerequisites</h2>
<p>None — this is beginner-friendly. You only need curiosity and a willingness to try prompts hands-on. Access to ChatGPT (free version works) or the OpenAI API is helpful but not required to learn the concepts.</p>

<h2>Why this matters for Indian learners</h2>
<p>Prompt engineering is one of the fastest-growing skills in tech hiring across India. Companies like TCS, Infosys, Wipro, and dozens of startups in Bangalore, Mumbai, and Hyderabad are actively hiring for AI and prompt engineering roles. As India's tech sector shifts toward AI-driven products, knowing how to work with GPT models—not just use them—gives you a real edge in competitive job markets.</p>
<p>Learning from OpenAI directly also signals to employers that you're learning from industry leaders, which matters in India's merit-driven hiring landscape. Whether you're aiming for a role in product, marketing, content, or engineering, prompt engineering is a skill that compounds your value.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes. This is OpenAI's official guide, freely available on their platform. You pay nothing to access it.</p>
<h3>How long will it take to complete?</h3>
<p>The course is designed for about 3 hours total. Spread it over one week—roughly 30 minutes a day if you practice with examples alongside reading. Go slower if you want to experiment with your own prompts.</p>
<h3>Will I get a certificate?</h3>
<p>This course doesn't offer a formal certificate. However, the skills you learn are directly applicable—you can share completed prompt projects or examples with potential employers as proof of capability.</p>

GPT, ChatGPT, API. Builders of the foundation.

OpenAI

Official OpenAI guide to prompt engineering — six strategies for getting better results from GPT models.

Prompt Engineering Guide

<h2>About this course</h2>
<p>The OpenAI Cookbook is a hands-on collection of code examples and practical guides straight from OpenAI engineers. Whether you're building with embeddings, retrieval-augmented generation (<a href="/topics/rag">RAG</a>), function calling, or <a href="/topics/fine-tuning">fine-tuning</a>, you'll find working code you can adapt immediately. This course matters because it bridges the gap between API documentation and real-world application — you learn not just *what* these tools do, but *how* to use them effectively in production systems.</p>

<h2>What you'll learn</h2>
<ul>
<li>How to use embeddings to convert text into vector representations and build semantic search systems</li>
<li>Retrieval-augmented generation (RAG) patterns to ground AI responses in your own data and reduce hallucinations</li>
<li>Function calling to enable language models to trigger actions and integrate with external tools</li>
<li>Fine-tuning techniques to customize models for your specific use cases and domains</li>
<li>Practical patterns for building chatbots, agents, and multi-step AI workflows</li>
<li>How to structure prompts and API calls for reliability, cost-efficiency, and better outputs</li>
<li>Real working code examples you can copy, modify, and deploy in your own projects</li>
</ul>

<h2>Who this is for</h2>
<p>You're an intermediate programmer or data practitioner who wants to move beyond toy projects and build AI features that solve real problems. You're comfortable reading code and want to learn by doing, not by watching endless theory videos.</p>
<ul>
<li><strong>Full-stack engineers</strong> — level up your AI capabilities and build AI-native products that stand out in the job market</li>
<li><strong><a href="/for/data-scientists">Data scientists</a> and ML practitioners</strong> — learn production patterns for deploying language models and retrieval systems at scale</li>
</ul>

<h2>Prerequisites</h2>
<p>You should be comfortable reading and writing Python or JavaScript code. Familiarity with APIs and basic machine learning concepts (what embeddings are, for example) helps, but the course teaches concepts alongside code, so you can learn as you go.</p>

<h2>Why this matters for Indian learners</h2>
<p>AI engineering skills command premium salaries across Indian tech hubs — from Bangalore startups building AI products to multinational teams in Hyderabad and Mumbai. Companies like Flipkart, Amazon India, and Infosys are actively hiring engineers who can implement RAG systems, fine-tune models, and optimize LLM costs. Learning production-grade patterns from OpenAI's own cookbook positions you for roles in AI product teams, and gives you concrete projects to showcase in interviews or your portfolio.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes. The OpenAI Cookbook is completely free — no paid tier, no hidden costs. You'll need an OpenAI API key to run the examples, but the course material itself costs nothing.</p>
<h3>How long will it take to complete?</h3>
<p>Most learners complete it in 15 hours, which works out to about 2–3 focused sessions per week if you're learning alongside work. Time varies depending on how deep you dive into each example and how much you experiment with variations.</p>
<h3>Will I get a certificate?</h3>
<p>No formal certificate is offered, but you'll have working code projects and real skills you can demonstrate to employers — often more valuable than a certificate. Many learners showcase their implementations on GitHub as proof of competence.</p>

Code examples and how-to guides for OpenAI API — embeddings, RAG, function calling, fine-tuning, and more.

OpenAI Cookbook

<h2>About this course</h2>
<p>This course teaches you how to write prompts that actually work with GPT models like ChatGPT and GPT-4. Created by OpenAI—the team behind these models—it covers six concrete strategies that help you get better answers, clearer explanations, and more useful outputs. Whether you're new to AI or looking to unlock its real potential, mastering <a href="/topics/prompt-engineering">prompt engineering</a> is the most practical skill you can learn right now.</p>

<h2>What you'll learn</h2>
<ul>
<li>How to write clear, specific instructions that GPT models understand and follow</li>
<li>The importance of giving context and examples—and exactly how to structure them</li>
<li>How to break complex tasks into smaller steps for more reliable results</li>
<li>When and how to ask the model to adopt a role or persona to improve outputs</li>
<li>Techniques for refining your prompts based on the results you get</li>
<li>How to use formatting, delimiters, and templates to guide model responses</li>
<li>Real examples from OpenAI showing each strategy in action</li>
</ul>

<h2>Who this is for</h2>
<p>You're the right fit if you've used ChatGPT or another GPT model but felt like it could do better. This course is for anyone—student, professional, entrepreneur, or curious learner—who wants to stop fumbling with prompts and start getting reliable, high-quality results.</p>
<ul>
<li><strong><a href="/for/students">Students</a>:</strong> Learn to use AI as a study and learning tool—better summaries, clearer explanations, and smarter brainstorming for assignments.</li>
<li><strong><a href="/for/working-professionals">Working professionals</a>:</strong> Speed up your work with sharper prompts for writing, analysis, coding help, and problem-solving on the job.</li>
</ul>

<h2>Prerequisites</h2>
<p>None—this course is beginner-friendly. You just need access to a GPT model (like ChatGPT) and curiosity about how to use it better. No coding, no prior AI knowledge required.</p>

<h2>Why this matters for Indian learners</h2>
<p>AI skills are now table stakes in India's job market. From tech startups in Bangalore to consulting firms in Mumbai, companies are hiring for "AI-ready" roles—and knowing how to actually use these tools gives you a real edge. Whether you're eyeing a data science role, content work, or just want to stay relevant in your current job, prompt engineering is the fastest way to add a high-demand skill to your CV. Many Indian tech companies and new-age startups are actively looking for people who can bridge the gap between business needs and AI—and this course gets you there.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes, completely free. OpenAI offers this as a public guide on their platform—no hidden costs or paid upgrades.</p>
<h3>How long will it take to complete?</h3>
<p>About an hour start to finish. You can easily finish this in one sitting, or work through one strategy at a time over a few days. Try out each technique as you learn it—that's where the real learning happens.</p>
<h3>Will I get a certificate?</h3>
<p>This course doesn't offer a formal certificate, but you'll have something more valuable: practical skills you can immediately use in your work and projects. The real proof is in the better results you get when you use these strategies.</p>

Six concrete strategies OpenAI recommends for writing better prompts that get better results.

Best Practices for Prompting GPT Models

<h2>About this course</h2>
<p>Learn to build reliable AI systems by mastering OpenAI's Evals framework — an open-source tool for rigorously testing <a href="/topics/llm">large language models</a>. This course takes you beyond casual prompting to the engineering discipline of LLM evaluation, taught through OpenAI's battle-tested approach. If you're serious about deploying AI responsibly, this is where you'll gain the skills to measure what actually matters.</p>

<h2>What you'll learn</h2>
<ul>
<li>Design and write custom evaluation scripts using OpenAI's Evals framework</li>
<li>Distinguish between different evaluation strategies and choose the right one for your use case</li>
<li>Measure LLM performance on tasks critical to your product or application</li>
<li>Automate testing workflows to catch model drift and regressions</li>
<li>Interpret evaluation results and iterate on prompts or <a href="/topics/fine-tuning">fine-tuning</a> based on data</li>
<li>Build repeatable benchmarks for comparing model versions and providers</li>
<li>Apply real-world evaluation patterns used by OpenAI's own teams</li>
</ul>

<h2>Who this is for</h2>
<p>You're ready for this course if you've worked with LLMs already and want to move beyond guesswork. Whether you're building AI products, integrating LLMs into business workflows, or conducting <a href="/topics/ai-research">AI research</a>, you'll learn the systematic approach to validation that separates production-ready systems from experimental prototypes.</p>
<ul>
<li><strong>AI engineers and ML practitioners</strong> — gain the evaluation toolkit you'll need to benchmark models and justify architectural choices in real projects.</li>
<li><strong>Product <a href="/for/managers">managers</a> and technical founders</strong> — understand how to measure LLM quality objectively, de-risk launches, and make data-driven decisions about model selection.</li>
</ul>

<h2>Prerequisites</h2>
<p>Comfort with Python and hands-on experience working with LLMs or language models (via API or local models). You should understand what prompts are and have run at least a few LLM calls before. No formal background in machine learning evaluation required.</p>

<h2>Why this matters for Indian learners</h2>
<p>India's AI startup ecosystem is growing rapidly, and companies building AI-first products — from SaaS platforms to customer-facing applications — urgently need engineers who can evaluate model quality systematically. Major tech employers like Flipkart, Amazon India, and emerging Indian AI startups increasingly hire for AI engineering roles that demand hands-on evaluation skills. LLM evaluation expertise remains a gap in the Indian market, making this a high-demand, differentiated skill that directly improves your hiring prospects and salary negotiation position.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes — completely free. The GitHub repository and all course materials are open-source.</p>
<h3>How long will it take to complete?</h3>
<p>Plan for about 5 hours of focused work. You can move through it in a week at a relaxed pace, or over two weeks if you're working through code examples hands-on (which we recommend).</p>
<h3>Will I get a certificate?</h3>
<p>This course doesn't offer a formal certificate, but you'll build tangible proof of skill — working evaluation code you can show employers or include in a portfolio.</p>

OpenAI's open-source framework for evaluating LLMs and LLM systems — learn how to write evals.

Evaluating AI Systems with OpenAI Evals

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