Five hours to go from how generative models actually work to writing prompts you can rely on - effective prompt design, advanced techniques like few-shot and chain-of-thought, then refining and evaluating the output.
By Board Infinity · Offered on Coursera
Rating and enrollment as listed on the Coursera course page.
27 short lessons, each prompt written and run in front of you
Shareable on LinkedIn the day you finish
7 assignments plus a discussion prompt across two modules
Most people meet Generative AI by typing a single line and hoping. This course starts one level below that - how the models work - and ends with prompts you can measure, refine and reuse.
Generative AI: Prompt Engineering Basics is an introduction to Generative AI and to prompt engineering as a discipline. It opens with what these models are and how they work - the evolution of the field, the key model families and what each is suited to - so that everything you write afterwards has a reason behind it rather than being guesswork.
From there it is about the prompt itself: the principles behind an effective one, the pitfalls that quietly ruin them, and the techniques that lift a weak result - zero-shot and few-shot learning, Chain-of-Thought and Tree-of-Thought, creative and specific modifiers. Module two turns to optimization: refining and iterating, the metrics that tell you whether a change actually helped, and the platforms built for this work.
It assumes no programming background. You write in plain language throughout, with Google Gemini as the course tool, and practical implementation lessons applying it to content creation, problem-solving and industry-specific work. The course closes on ethics and responsible use, and on where prompt engineering is heading next.
No technical background is assumed anywhere in the course. If one of these is you, it was built for exactly this starting point.
You already paste work into an assistant, then fix most of what comes back. The refinement and iteration lessons are about closing exactly that gap.
You have heard the terms and never used the tools properly. Module one starts at what a generative model is and assumes nothing.
Model families, how prompts shape responses, few-shot and chain-of-thought - the groundwork every later AI course assumes you have.
Writing, SEO, digital marketing, multimedia and repeatable workflows - the applied half of the course is aimed straight at this.
These are the four outcomes Coursera lists for the course. Each one is the product of a specific run of lessons rather than a general aspiration.
How generative models work and how they evolved, the key model families and their use cases, and why prompt engineering is the skill that decides what you get - applied to text, code and automation.
The principles behind a prompt that returns accurate, context-aware, high-quality output across domains - and the common pitfalls in prompt design, so you can see why a prompt failed.
Zero-shot and few-shot learning, Chain-of-Thought and Tree-of-Thought, creative and specific modifiers, and structured experiments with specificity and context to optimize the output.
The metrics that evaluate prompt performance, iterative refinement for accuracy - and addressing bias and ensuring responsible use, so what you ship is defensible as well as good.
A dedicated coach who works with these tools for a living - someone who can read the prompt you have rewritten six times and tell you what is actually wrong with it.
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The output you cannot get right, diagnosed by someone who has hit the same wall.
Not just whether the answer looks fine - whether the prompt will work again tomorrow.
Real briefs from your own work, set and then reviewed, until the technique is habit.
Where AI actually saves you time in your job - marketing, analysis, support or code.
Five hours end to end, fully self-paced. Every module lists its learning objectives, every lesson inside it with its duration, and what you leave with.
Module structure, lesson titles and durations as published on the Coursera course page. Coursera occasionally renames or re-sequences items.
Nothing to install and nothing to pay for before you start - the course tool runs in a browser tab.
The tool Coursera lists for this course, and where the practical implementation lessons are demonstrated.
Covered as the key model families - what each architecture is built for, so you can pick the right one for a task.
A dedicated lesson on the platforms built for serious prompt work, and what each one adds beyond a chat window.
Explored in module two - how prompting changes when the output is not text, and where each format is useful.
The eight capabilities Coursera lists for this course, grouped the way the work actually splits - understanding the model, writing the prompt, and checking what came back.
Knowing what the thing is before you argue with it.
Getting the output you actually meant, repeatably.
Whether it improved, and whether you should ship it.
Finish the graded work and Board Infinity issues a shareable certificate through Coursera - with a public verification link and a one-click path onto your LinkedIn profile.
Issued on successful completion of the course and its 7 assignments across the two modules.
Filed under Licenses & Certifications, where recruiters actually look.
Its own verification link, so a hiring manager confirms it in seconds.
Awarded on 7 graded assignments - it certifies what you can do.
Board Infinity as the provider, Coursera as the platform.
Not a job title for most people - a skill that now sits inside the one they already have. These are the places it shows up first.
Briefs, drafts, outlines and metadata at a different pace - with a lesson dedicated to SEO, blog writing and digital marketing.
Code generation and automation prompting - the difference between a snippet you trust and one you have to rewrite.
Repeatable prompts inside workflows, so a task that used to take an afternoon runs the same way every time.
Problem-solving and analysis prompting, plus the evaluation metrics that keep you honest about what the model produced.
Bias, responsible use and the limits of these systems - increasingly the part of an AI project someone has to own.
The closing lesson maps trends, career paths and emerging technologies, so you know what to learn after this.
Every technique in this course is discoverable somewhere on the internet, and the tools themselves cost nothing to try. Nobody is pretending otherwise. What a course adds is the order you learn it in, the checking that makes it stick, and something to show at the end.
This course stands on its own. These are the Board Infinity courses that assume it and build straight on top.
The wider story behind module one - how the field got here, the milestones and the technologies underneath the tools you are prompting.
View course Apply it to contentThe natural next step once your prompts are reliable: content creation, personalisation and campaign work at scale.
View course Apply it to codeTakes the code generation and automation lesson much further - AI-assisted coding, debugging and workflow automation.
View courseBoard Infinity runs 270+ courses and 57 specializations on Coursera - browse the full catalogue.
Everything learners ask before enrolling, answered plainly.
Prompt engineering is the practice of writing the input you give a generative AI model so that it returns the output you actually wanted. It covers how you state the task, the context and constraints you supply, the examples you include, the format you ask for, and how you revise the prompt when the first answer misses. This course teaches it as a method rather than a list of tricks: the principles behind an effective prompt, the common pitfalls in prompt design, and techniques including zero-shot and few-shot learning, Chain-of-Thought and Tree-of-Thought.
Because the quality of what these tools give back is mostly decided by what you put in, and most people never get past a one-line request. Five hours here is the difference between an AI that produces something you have to rewrite and one that produces something you can use - across writing, analysis, code and automation. It's also a skill that transfers: the techniques work the same way whichever model you happen to be using.
Coursera lists eight for this course: Generative Model Architectures, LLM Application, Model Evaluation, Prompt Patterns, Debugging, Responsible AI, Prompt Engineering Tools and AI literacy. In plain terms: you'll understand how generative models work and where they fail, write prompts that hold up, debug one that isn't working, measure whether a change actually improved the output, and use these tools responsibly.
No. The course is rated Beginner and assumes no coding background - you write prompts in plain language, not code. One lesson covers prompt engineering for code generation, but it's about directing the model rather than writing programs yourself, so you can follow it without having programmed before.
Google Gemini is the tool Coursera lists for the course. Alongside it, the lessons cover the wider landscape - key model families such as GPT, BERT and T5 and what each is suited to - and a lesson on platforms for advanced prompt engineering including IBM watsonx and LangChain. The techniques themselves are model-agnostic: what you learn on one assistant carries over to the others.
Yes - that's a large part of it. Both modules include practical implementation lessons: content creation and writing tasks, problem-solving and analysis, and industry-specific applications. Module two goes further into using AI for SEO, blog writing and digital marketing; prompt engineering for code generation and automation; and multimedia AI across image, video and audio generation.
Yes. Four lessons are explicit practical implementations - content creation and writing tasks, problem-solving and analysis, and industry-specific applications - and the prompting techniques are demonstrated on screen as they're introduced. Seven graded assignments and a discussion prompt then check that you can apply them rather than just recognise them.
About 5 hours in total - roughly 2 hours for module one and 3 hours for module two. It's fully self-paced with flexible deadlines, so most learners finish inside a week, and an unhurried afternoon and evening is enough to get through it.
The price is shown at checkout on the Coursera course page and can vary by region. It's included at no extra cost with a Coursera Plus subscription, and Coursera's financial aid is available to eligible learners.
Yes - this page is Board Infinity's own overview of our course. Enrollment, payment, video lessons, grading and the certificate all happen on Coursera, where the course is hosted. Think of this page as the detailed tour before you head there.
Yes. Completing the course and its 7 assignments earns a shareable certificate from Board Infinity, issued through Coursera with its own verification link. Coursera's one-click button adds it straight to the Licenses & Certifications section of your LinkedIn profile.
By cutting the loop between asking and getting something usable. Most of the time lost with these tools goes on vague prompts, re-asking and rewriting the output by hand. The module on refining and iterating on prompts, and the one on evaluation metrics, are about exactly that: getting to a good answer in fewer attempts, then reusing the prompt that worked.
It's a foundation, not a guarantee - no course can promise a role or a raise. What five hours here realistically gives you is a skill that's now expected inside a lot of existing jobs: writing prompts that work, evaluating the output and using AI responsibly. For a dedicated AI role you'd build on it, and the closing lesson on trends and career paths is there to point at what comes next.
It depends where you're aiming. For a deeper foundation, The Rise of Generative AI covers how the field developed. For content, marketing and SEO work, Generative AI in Marketing. For AI-assisted coding and automation, Generative AI in Software Development. The Where to go next section links all three.
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