AI Literacy: Plain-English Explainers for Working Professionals
Clear explanations of generative AI, prompt engineering, AI agents, and the other terms you keep hearing in meetings, in the order that actually makes sense to learn them.
The Right Order to Learn Generative AI, Prompt Engineering, and AI Agents
These six guides do not stand alone. Read them in this order and each one makes the next easier to follow. Already know what you need? Jump straight to the guide.
What Is a Generative AI Course?
Start with what generative AI actually is. Everything else on this page builds on this.
Read the Guide →What Is Prompt Engineering?
Once you know what generative AI does, this covers how you actually talk to it.
Read the Guide →- Coming soonWhat Are AI Agents, Explained
Builds on prompting: what happens when an AI tool takes several steps on its own instead of answering one question.
- Coming soonWhat Is RAG (Retrieval-Augmented Generation)?
Covers how AI tools stay accurate by checking real documents and data, relevant to both prompting and agents.
- Coming soonAI Workflow Automation Explained
Where prompting, agents, and RAG come together in an actual day-to-day workflow.
- Coming soonGenerative AI vs Data Science Course: What Is the Difference?
Once the concepts click, this helps you decide which type of course to look at next.
How These Concepts Actually Connect
Unlike subsidies or skills, these ideas are not five separate topics. They are one idea with four extensions. Here is the shape of it.
Generative AI is the foundation. Prompt engineering is how a person directs it. AI agents are what happens when that direction extends across several steps instead of one. RAG is an accuracy layer that cuts across both prompting and agents. Workflow automation is where all three come together in practice. Generative AI versus data science sits to one side as a decision about your next step.
RAG connects prompting and agents as a shared accuracy layer.
Deciding your next step
What People Get Wrong About AI
A few widely believed ideas about generative AI that do not quite hold up, and why the distinction matters.
| The Misconception | What Is Actually True |
|---|---|
| “Generative AI always gives accurate answers” | It can produce confident, well-written responses that are still factually wrong, a pattern often called hallucination. Output is worth checking before you rely on it for anything important. |
| “AI agents can fully replace human judgement” | Agents can carry out several steps toward a goal on their own, but they still follow instructions and can make mistakes across those steps. Review still matters, especially for anything with real consequences. |
| “Prompt engineering is a technical skill only developers can learn” | It is mostly about writing clear, specific instructions. Most of the skill involved is communication, not programming. |
| “If a course covers ChatGPT, it has covered generative AI” | ChatGPT is one interface built on generative AI. Generative AI is the broader category, and other tools apply it differently. |
| “AI understands context the way a person does” | It identifies patterns in data rather than understanding meaning the way a person would, which is part of why it can still get context-dependent questions wrong. |
The Jargon Buster
A few phrases you have probably nodded along to without being totally sure what they meant. Here is the honest version.
| You Might Have Heard | What It Actually Means |
|---|---|
| “AI-powered” | Means a generative AI model is used somewhere in the product. It does not tell you how well it works, or whether AI was even the right tool for that particular job. |
| “Cutting-edge AI” | Usually just means recently released. A newer model is not automatically more useful for what you specifically need to do. |
| “AI agent” | A system that can carry out several steps toward a goal on its own, rather than only answering one question. Worth checking exactly what it is allowed to do before assuming it handles everything unsupervised. |
| “Prompt engineering” | A formal name for writing clear instructions to an AI tool. If you have ever rephrased a question to get a better answer, you were already doing a basic version of this. |
| “Machine learning” vs “generative AI” | Machine learning is the broader field. Generative AI is one application of it, specifically the kind that creates new text, images, or code. |
A Few More Terms Worth Knowing
These come up often alongside the six concepts above but do not have their own guide yet. Short definitions for now, fuller entries are planned for the site's Glossary as it is built out.
| Term | Plain-English Definition |
|---|---|
| LLM (Large Language Model) | The type of AI model trained on large amounts of text that powers many generative AI tools, including most chatbots. |
| Hallucination | When an AI tool produces an answer that sounds confident and plausible but is factually incorrect. |
| Token | A small unit of text, often part of a word, that an AI model processes one at a time when reading or generating language. |
| Context Window | The amount of text an AI tool can work with at once in a conversation, before earlier parts stop being taken into account. |
| Fine-Tuning | Additional training on a specific data set to make a general AI model more accurate for a particular use case. |
| Multimodal AI | An AI tool that can work with more than one type of input or output, such as text, images, and audio, rather than text alone. |
Frequently Asked Questions
Quick answers on how to use these guides, and what understanding AI concepts can and cannot do for you.
Do I need to read these guides in the suggested order?
No. The Start Here path is a suggestion based on how the concepts tend to build on each other, not a requirement. If you already know what generative AI is and just need to understand AI agents, skip ahead.
Do I need to understand all of these terms before taking an AI course?
No. These explainers are meant to help you follow along and ask better questions, not act as a prerequisite. Most courses introduce the relevant terms as you go.
What is a large language model (LLM), and is it the same as generative AI?
A large language model is a specific type of AI model trained on text, and it powers many generative AI tools. Generative AI is the broader category, and it also includes tools that generate images, audio, or video.
What is the difference between AI literacy and AI skills?
AI literacy means understanding what these tools and concepts are and how they generally work. AI skills refer to actually using them in your own tasks. Literacy usually comes first, but it does not replace hands-on practice.
Does understanding these concepts guarantee I will get a promotion or a higher salary?
No. Understanding AI concepts may help you use these tools more effectively, but no explainer, course, or skill can guarantee a promotion, a salary increase, or a job outcome.
Where should I start if I am completely new to AI?
Step one of the Start Here path above, the generative AI explainer, since most of the other terms on this page build on it.
Once the Concepts Click, Here Is Where to Go Next
AI literacy connects to two other parts of this site, depending on what you are actually trying to figure out.
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