Understanding Generative AI
Generative AI writes, summarizes, codes, and creates images, but what is it actually doing? This course explains how it works at a practical level, without math or jargon. Once you understand the mechanics, you can predict where it will be brilliant, where it will fail, and why. You'll finish with a set of everyday habits for using it with confidence and good judgment.
Course details
Duration
1h 20m 33s
Lectures
15
Video
1.5 hours
Quizzes
1
Level
Beginner
Course Description
You don't need to know how to build an AI model to use one well, but knowing roughly how it works changes everything. This course explains what generative AI is doing when it produces text, images, audio, and video, so its strengths and failures stop being surprises.
What you'll learn
- How generative AI differs from predictive AI, and how a language model writes one token at a time
- Training, fine-tuning, the knowledge cutoff, and the context window
- How image, audio, and video models work, and the risks of deepfakes and likeness
- Why hallucinations happen, and where models are weak at math, dates, and citations
- Bias, the default AI voice, and the data rules for approved tools
- Writing good requests, iterating, and verifying output before it leaves your hands
Who it's for
Every employee who wants a solid, working understanding of generative AI. No technical background required.
15 short video lessons in three modules: What Generative AI Is, What It Can and Cannot Do, and Using It With Judgment.
Module 1: What Generative AI Is
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1.1 Generative vs. Everything ElseThe difference between AI that classifies or predicts and AI that produces new text, images, audio, and code, and why that difference changes how you use it.
In this lesson:
- Distinguish predictive AI from generative AI
- Explain what a model is and what training means, in plain terms
- Treat generative output as a draft you check and own
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1.2 How a Language Model Produces Text
Tokens, next-word prediction, and why fluent output is not the same as verified truth, explained without math.
In this lesson:
- Explain what a token is
- Describe how a language model writes a reply, one piece at a time
- Explain why fluent output is not evidence of accuracy
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1.3 Training, Tuning, and the Knowledge Cutoff
Where a model's knowledge comes from, why it has a date, and what fine-tuning and instructions change.
In this lesson:
- Explain what training gives a model and what it doesn't
- Account for the knowledge cutoff in everyday use
- Distinguish what fine-tuning and instructions can and cannot change
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1.4 Images, Audio, and Video Models
How generative media works at a working level, what it does well, and the tells and risks: likeness, rights, and deepfakes.
In this lesson:
- Explain at a working level how image, audio, and video models produce media
- Recognize what generative media does well and its common tells
- Respond safely to the risks of likeness, rights, and deepfakes
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1.5 The Context Window: What the Model Can Actually See
What you paste in versus what it remembers, why long inputs get lost, and how retrieval and connected documents change the picture.
In this lesson:
- Explain the context window and what it contains
- Keep long inputs and long conversations from losing key details
- Describe how retrieval works and what to check when a tool cites a source
Module 2: What It Can and Cannot Do
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2.1 The Strengths
Drafting, summarizing, transforming, explaining, brainstorming, and coding: the tasks where generative AI reliably saves hours.
In this lesson:
- Name the six tasks where generative AI reliably saves time
- Recognize a strong fit versus a weak fit for a task
- Capture the time saving without skipping the check
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2.2 Hallucination, Explained
Why models produce confident fabrications, the situations that trigger it, and the checks that catch it.
In this lesson:
- Define hallucination and explain why it happens
- Recognize the situations that make hallucination more likely
- Apply four checks that catch fabrications before they leave your hands
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2.3 Reasoning, Math, and Facts
Where models are strong and weak at logic, numbers, dates, and citations, and how to structure tasks so they succeed.
In this lesson:
- Map where models are strong and weak at reasoning, numbers, dates, and citations
- Explain why numbers slip in generated text
- Structure tasks so the model and reliable tools each do their part
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2.4 Bias, Tone, and Sameness
How training data shapes outputs, why drafts sound alike, and how to keep your own voice and judgment.
In this lesson:
- Explain how training data shapes bias and default assumptions
- Recognize the default AI voice and replace it with your own
- Review AI drafts for fairness before they reach people
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2.5 Privacy, Data, and the Tools You're Allowed to Use
What happens to what you type, consumer versus enterprise tools, and the everyday data rules.
In this lesson:
- Describe what happens to text you enter into a generative AI tool
- Distinguish consumer tools from enterprise tools
- Apply the everyday data rules and report slips promptly
Module 3: Using It With Judgment
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3.1 Writing a Good Request
Context, task, format, and examples: the four parts of a request that works, with before-and-after prompts.
In this lesson:
- Write a request with context, task, format, and examples
- Diagnose a thin request and improve it
- Use small habits that make good requests quick to write
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3.2 Iterating Instead of Accepting
Treating the first answer as a draft: refine, constrain, ask for alternatives, ask it to critique itself.
In this lesson:
- Treat the first answer as a draft that reveals what the request left out
- Use four iteration moves: refine, constrain, alternatives, critique
- Know when to stop iterating and finish by hand
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3.3 Verifying Output Before It Leaves Your Hands
The plausibility check, source check, and number check, scaled to the stakes of the task.
In this lesson:
- Apply the plausibility, source, and number checks
- Scale checking effort to the stakes of the task
- Handle claims that cannot be verified in time
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3.4 Three Composite Cases
The fabricated reference, the confident wrong number, and the summary that dropped the caveat: what went wrong and the habit that prevents it.
In this lesson:
- Analyze three common generative AI failures in everyday work
- Explain why the model behaved as it did in each case
- Adopt the habit that prevents each failure
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3.5 Your Generative AI Habits
The one-page habit sheet, the pre-send checklist, and your next task with an AI assistant; the finale.
In this lesson:
- Apply the one-page habit sheet to any task
- Run the pre-send checklist before output leaves your hands
- Complete Understanding Generative AI
Understanding Generative AI — Final Exam
Learn at your own pace
Study the materials at your convenience, you can always return to them to repeat
Certificate of completion
Complete every lesson and pass the final exam to earn a RiseCert certificate of completion you can download and share.
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Understanding Generative AI
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Got Questions? We've Got Answers!
Answers to the most common questions about this course.
What is the Understanding Generative AI course?
Understanding Generative AI is a RiseCert online course that explains, in plain language, what generative AI is actually doing when it writes, summarizes, codes, or creates images, so you can predict where it will shine and where it will fail. It includes 15 video lessons (about 1 hour 20 minutes) and a final exam.
Who should take a generative AI fundamentals course?
Understanding Generative AI is designed for every employee who uses, or will soon use, AI assistants at work, in any role or industry. It suits people who want a solid working understanding of how these tools behave, managers setting expectations for their teams, and anyone who has been surprised by a confident wrong answer.
Do I need a technical background or math skills to take it?
No. The course has no math and assumes no technical background. Concepts such as tokens, training, the knowledge cutoff, and the context window are explained with everyday comparisons, like a phone keyboard suggesting your next word or a model's working desk, and each lesson ties back to practical habits for your own work.
What is the difference between generative AI and predictive AI?
Predictive AI sorts, scores, and forecasts: a spam filter, a fraud score, or a demand forecast, with output chosen from a fixed menu of labels or numbers. Generative AI produces new content, such as text, images, audio, video, or code, from a request. Understanding Generative AI opens with this distinction because it changes how you should check the output.
Why does generative AI make things up?
A language model writes by repeatedly predicting a likely next token, and that loop has no built-in fact-check. When the facts aren't in its training or its context window, it still produces the most plausible words, which can be a confident, specific fabrication called a hallucination. The course covers the common triggers and four checks that catch them.
What are a knowledge cutoff and a context window?
The knowledge cutoff is the date a model's training stopped; it learns nothing after that unless a tool supplies current information. The context window is everything the model can see at once, measured in tokens: instructions, your messages, attachments, and its replies. Understanding Generative AI explains both and how they cause out-of-date or incomplete answers.
What topics does Understanding Generative AI cover?
Three modules: What Generative AI Is (predictive vs. generative, tokens, training and the cutoff, image, audio, and video models, the context window); What It Can and Cannot Do (strengths, hallucination, math and dates, bias and sameness, privacy and approved tools); and Using It With Judgment (good requests, iterating, verifying, composite cases, and a habit sheet).
What will I be able to do after completing the course?
You'll be able to tell strong-fit tasks from weak ones, write a four-part request, iterate instead of accepting the first answer, spot hallucination triggers, move arithmetic and date math to a spreadsheet or calendar, keep work data in approved tools, and run plausibility, source, and number checks scaled to the stakes before output leaves your hands.
Does the course explain AI privacy and which tools are safe for work data?
Yes. A dedicated lesson explains what happens to text you type into an AI tool, the difference between consumer and enterprise tools, and everyday data rules: approved tools only, never paste credentials, share the minimum necessary, and report slips the same day. Your organization's own policy always sets the specifics and wins where it differs.
Which AI tools or products does it teach?
The course is vendor-neutral. It teaches how generative AI works and how to use it with judgment across chat assistants, writing tools, image, audio, and video generators, and assistants connected to company documents. No specific product is required or endorsed, so the habits apply to whichever tools your organization has approved.
How is this different from a prompt-writing course?
Prompt-writing courses focus on phrasing requests. Understanding Generative AI starts with how the technology works, then uses that to explain its strengths, its failures, and its data risks. Writing good requests is one lesson in Module 3, alongside iterating, verifying output, and a pre-send checklist, so you understand why the techniques work.
Is the course content up to date?
Yes. The course describes generative AI tools as of September 2026, including web search, connected documents, memory features, and built-in calculation. Because these tools change quickly, it focuses on durable mechanics, such as next-token prediction, training cutoffs, and the context window, plus checking habits that stay useful as products change.
How long does the course take to complete?
The course has about 1 hour 20 minutes of video across 15 lessons, plus a final exam. Lessons typically run five to six minutes and are organized into three modules: What Generative AI Is; What It Can and Cannot Do; and Using It With Judgment. It's easy to complete in a few sittings.
Is Understanding Generative AI online and self-paced?
Yes. The course is 100% online and self-paced, and your access does not expire. You can start, pause, and resume at any time on a computer, tablet, or phone with a modern web browser. There is nothing to install and no fixed schedule to follow.
Is there a final exam?
Yes. The course ends with a 30-question multiple-choice final exam covering all three modules. You need a score of 80% or higher (24 of 30) to pass, and you can retake the exam as many times as you need. Complete all 15 lessons and pass the exam to finish the course.
Do I get a certificate of completion?
Yes. After you complete all 15 lessons and pass the final exam, you can download a RiseCert certificate of completion from your account. It is a course completion certificate, not a license, a professional certification, or a continuing education (CE) certificate.
Does this course count for continuing education (CE, CPE, or CEU) credit?
No. Understanding Generative AI is not currently approved or accredited for continuing education credit, including CE, CPE, or CEU, by any state or licensing body. If you need credit for a license or professional designation, choose a course approved by that body. This course builds practical AI understanding rather than credit hours.
Is this course legal, security, or professional advice?
No. The course is educational and does not provide legal, privacy, security, or other professional advice. It teaches general habits for using generative AI responsibly. Your organization's AI and data policies set the specifics and take precedence wherever they differ, and questions about a specific situation should go to your manager, IT, or legal team.
How much does the course cost, and can I enroll my team?
Understanding Generative AI costs $99 per learner as a one-time purchase, with no subscription or recurring fees. To enroll a team or an entire organization, contact RiseCert through the Contact Us page at risecert.org.
What is the refund policy?
All sales are final and no refunds are issued, as stated in the RiseCert Terms of Use. Please review the course description, lesson list, and these FAQs before purchasing, and contact RiseCert with any questions first.