Beginner’s Guide to Generative AI (2025): What It Is and How to Use It

Generative AI is reshaping industries, and it’s happening fast. According to a 2025 report by McKinsey, 71% of organizations now use generative AI in at least one business function, up from 65% just a few months earlier. Adoption is growing rapidly across marketing, software development, customer service, and more.

I recently finished the Generative AI for Beginners course on Udemy. It’s a solid intro for anyone starting from zero and wanting to make sense of how tools like ChatGPT really work, and how to start using them. It inspired me to dig deeper, test some tools for myself, and put together this beginner’s guide to help others do the same.

This blog is your beginner’s guide to generative AI. It takes lessons from the course, redelivers them in plain language, and layers in real-world tools, examples, and trusted sources to make things stick.

Let’s dive in.

What Is Generative AI?

What is generative AI, exactly? At its core, it’s a type of artificial intelligence designed to create things, like text, images, audio, video, or even code. Instead of just analyzing data or answering yes-or-no questions, generative AI can produce brand-new content that feels surprisingly human.

If you’ve ever used ChatGPT, asked Midjourney to create an image, or watched a deepfake, you’ve seen it in action.

A simple way to think about it is predictive text on steroids. You know how your phone suggests the next word when you’re typing a message? Generative AI works in a similar way, but on a massive scale, predicting one word (or pixel or note) at a time, based on the patterns it learned from billions of examples.

It’s called “generative” because the output is new, not copied. The AI doesn’t pull answers from a database, it builds responses word by word, based on everything it’s learned during training.

These models are trained on enormous datasets. For example, OpenAI’s GPT-4 was trained on a wide range of publicly available text, books, websites, code repositories, and more, to help it understand how humans communicate. Other models, like Anthropic’s Claude and Meta’s LLaMA, follow similar principles.

While most people know generative AI through tools like ChatGPT, it’s a much broader field. It powers:

• Text generation: blog posts, emails, summaries
• Image creation: using DALL·E, Midjourney, or Stable Diffusion
• Voice synthesis: tools like ElevenLabs or PlayHT
• Music and audio: Suno or Boomy
• Code generation: GitHub Copilot, Replit Ghostwriter

As you’ll see throughout this guide, these tools are already changing how people work across industries, from marketing and design to software development and education.

Key Terms in Generative AI (Explained Simply)

Before you start using generative AI tools effectively, it helps to understand a few foundational terms. These concepts are the building blocks behind models like ChatGPT, Claude, and DALL·E, and knowing them makes everything else make more sense.

Prompt

A prompt is any input you give to an AI model. It could be a question, a request, or even just a phrase. For example:
“Summarize this blog post in one sentence,” or “Write a product description for hiking boots.”

The AI uses your prompt as a jumping-off point to generate a response. The quality of that response depends heavily on how well you phrase the prompt. A vague prompt usually gives you a vague answer. A clear, specific prompt with context gives you something far more useful, and much closer to what you actually want.

If you want to go deeper into how prompting works across different models and use cases, I’ve written a full 2025 guide to prompt engineering with examples, techniques, and real workflows. It’s geared toward beginners, but goes way beyond the basics.

You can also explore curated prompt patterns and methods on sites like PromptingGuide.ai, which regularly updates prompt structures for ChatGPT, Claude, and more.

Embedding

AI doesn’t “understand” language like we do. Instead, it turns words into numbers using embeddings, mathematical representations that capture meaning and context. Words that are similar in meaning (like “king” and “queen”) are positioned close together in this numerical space.

Embeddings make it possible for models like GPT-4 to compare concepts, understand relationships, and generate coherent responses, even if you don’t use the exact same phrasing. You can see how this works in practice in OpenAI’s official documentation on embeddings, where they explain how they’re used for search, clustering, and semantic similarity.

Transformers

The real breakthrough behind today’s generative AI tools is the transformer architecture, introduced by Google researchers in their landmark 2017 paper, “Attention Is All You Need”.

Transformers allow models to understand context across entire paragraphs instead of just a few words at a time. This is why tools like GPT-4 or Claude can handle long, complex instructions, they’re designed to prioritize and weigh different parts of your input using something called “attention mechanisms.”

If you want a visual, beginner-friendly explanation, the Illustrated Transformer by Jay Alammar breaks it down beautifully.

What Are Large Language Models (LLMs)?

If Generative AI is the toolbox, then Large Language Models are the power drill, the go-to tool for anything involving text. They’re fast, adaptable, and used across countless industries to write, summarize, translate, code, and more.

LLMs are trained to understand and generate language by predicting one word at a time, based on patterns they’ve learned from massive amounts of data. They don’t pull answers from a database. Instead, they generate responses on the fly, word by word, using everything they’ve been trained on.

If you’re curious about how to actually learn with AI instead of just using it, check out my post How to Use AI to Learn Faster (and Think Better). It dives into how understanding these models helps you ask smarter questions — and become a better thinker in the process.

Models like GPT-4, Claude 3, and Gemini are all built using this approach. They’ve each been trained on massive datasets — books, websites, research papers, forums, and more — totaling trillions of words and billions (or even trillions) of parameters. Those parameters are like tiny tuning knobs that help the model decide what to say next.

LLMs rely on the transformer architecture, which you read about in the Key Terms section. That structure helps them understand the full context of what you’re asking, not just the last word you typed.

What Can LLMs Actually Do?

LLMs are behind many of the tools you already use (or have seen online):

• Generate blog posts, emails, summaries, and product descriptions
• Power chatbots and customer service tools
• Translate between languages and rephrase content
• Write and debug code using tools like GitHub Copilot
• Pull insights from documents and streamline workflows

As these tools get better, they’re moving from novelty to necessity, especially for people working in content, customer support, development, or education.

Prompt Engineering: Getting Better Results from AI

Generative AI is only as good as the prompt you give it. A prompt is the input, your question, command, or instruction, and it shapes the AI’s output. The concept might sound simple, but it’s a skill that’s rapidly becoming essential. In fact, companies like Google Cloud now offer official guides on prompt engineering, and Anthropic recently published practical tips on how to get better responses from their Claude models, including ideas like breaking down tasks step by step and assigning the AI a role or persona.

A vague prompt like:

“Tell me about AI”

…will give you a generic answer. But something more specific like:

“Explain generative AI to a high school student using a real-world example”

…produces a much clearer, more helpful response. That’s the essence of prompt engineering, crafting better prompts to get better results.

Even small changes in wording, structure, or tone can make a big difference. Whether you’re writing emails, generating code, or brainstorming ideas, the quality of your prompt shapes the outcome.

Best Practices for Prompting

Here are a few quick tips that apply across most AI tools:

• Be specific about what you want
• Add context (who it’s for, what style, what format)
• Use examples or constraints
• Iterate, rework your prompt based on what the AI gives you
• Test different tools, not all models respond the same way

If you need some help writing and are feeling frustrated with stiff or robotic AI outputs, I walk through how to humanize AI-written content step-by-step in my Humanizing AI guide.

You can also grab my free downloadable prompt library (PDF), which breaks down over 25 types of prompts, from writing and research to brainstorming, role-based instructions, and structured workflows, so you can understand how each format works and when to use it.

Embeddings: How AI Understands Meaning

AI models don’t understand language the way humans do. Instead of interpreting words emotionally or contextually, they convert them into numbers, specifically, vectors called embeddings. These numerical representations allow models like GPT-4 or BERT to compare and generate language based on meaning, not just exact matches. OpenAI and Cohere describe embeddings as the core mechanism that helps AI detect nuance, similarity, and context within your input.

Imagine each word or phrase as a point in a multi-dimensional landscape. The more similar two concepts are in meaning, the closer they sit in that space. For instance, “physician” and “doctor” would be nearly overlapping, while “volcano” would be off in a different region entirely. This spatial relationship helps models understand when two different words are expressing a similar idea, which is why you can ask ChatGPT something in a dozen different ways and still get a coherent answer.

What’s powerful about embeddings is that they’re not static. They adjust depending on the surrounding words, so the word “pitch” in a music context lands somewhere completely different than “pitch” in a business meeting. This flexibility is part of what allows modern LLMs to handle ambiguity and nuance so well, as shown in Google’s research on semantic vector search.

Beyond conversation, embeddings are what drive features like AI-powered document search, smart recommendations, and personalized content delivery. Developers use APIs from Hugging Face, OpenAI, and Google Vertex AI to embed user queries, documents, or even product listings, then match them based on meaning, not keywords.

Fine-Tuning: Customizing AI for Specific Tasks

Out of the box, large language models are trained on massive, general-purpose datasets pulled from books, websites, code, and more. That makes them flexible, but sometimes, too general. If you want a model to specialize in a specific voice, topic, or industry, that’s where fine-tuning comes in.

Fine-tuning is the process of taking a base model and training it further on your own data. This allows the AI to learn your style, follow your structure, and understand your domain more deeply. Companies like Meta AI and NVIDIA have published frameworks for doing this across different use cases, from legal research to customer support.

There are several approaches to fine-tuning:

• Supervised fine-tuning: You provide input/output pairs (like a user question and your preferred response), and the model learns the pattern.
• Instruction tuning: The model is trained to follow specific instructions by seeing lots of task examples.
• Reinforcement Learning with Human Feedback (RLHF): Humans score outputs, and the model adjusts based on preference rankings. This was key to how ChatGPT became more helpful and safe.

But is this something beginners need to worry about?

Not really, at least not yet. Most beginners can do a lot with prompt engineering alone. Fine-tuning comes into play when you find yourself needing the same prompt structure over and over, or you want the AI to sound exactly like you or your brand without needing constant corrections.

Real-World Example

Let’s say you run a small online store and use ChatGPT to answer customer emails. At first, it works great. But over time, you get tired of repeating the same prompt: “Write a friendly, helpful reply using a casual but professional tone. Keep it under 100 words. Always include the refund policy at the end.”

With fine-tuning, you could train the model once on your preferred email style and tone, using past conversations as examples. Then, every time you ask a question like “How should I reply to this customer?”, the model responds exactly how you want, without extra prompting.

This kind of workflow is already being used by businesses of all sizes, as shown in Weights & Biases’ case studies, where teams train models on things like internal policies or customer support logs.

Fine-tuning takes more setup than prompting, but it offers more control. Think of it like training a generalist to become a specialist, one who knows your voice, your values, and your workflows.

Generative AI Use Cases Across Industries

Now that you understand the basics of generative AI, let’s look at where it’s already making an impact. From marketing to retail to software development, companies are using AI tools to speed up workflows, reduce manual work, and unlock new creative possibilities.

Software Development

Generative AI is streamlining the way developers build and debug code. Tools like GitHub Copilot and Replit Ghostwriter can suggest entire code blocks, explain error messages, and even translate code between languages.

In testing and QA, AI can generate detailed test cases and simulate edge scenarios to catch bugs earlier, something that previously required hours of manual setup. According to McKinsey, companies using generative AI in dev workflows report up to a 40% productivity boost.

Retail & E-Commerce

In retail, AI is transforming everything from product descriptions to customer support. Fashion brands use tools like Vue.ai to generate tailored descriptions for every size and color variation, saving hours per product drop.

Chatbots like Heyday help answer customer questions in real time, while AI-powered recommendation engines personalize shopping experiences based on real-time behavior. Behind the scenes, companies like Zalando and Shopify are experimenting with AI to optimize inventory and even predict returns.

Marketing & Content Creation

Generative AI is becoming a must-have tool in content workflows. Tools like Jasper, Copy.ai, and Writer help marketers draft blog posts, emails, product pages, and even ad copy in seconds.

Beyond writing, AI is also used for idea generation, campaign planning, and SEO optimization. Platforms like Surfer SEO combine content creation with keyword guidance to help teams rank better on Google, faster.

Healthcare (Emerging Use)

AI is beginning to assist with documentation, note summarization, and patient communication. For example, Nuance DAX, backed by Microsoft, helps doctors generate clinical notes from patient conversations.

Meanwhile, hospitals are experimenting with fine-tuned models to generate discharge summaries or patient-facing explanations of test results. Adoption is still early, but the potential is massive, especially for reducing burnout and admin time.

Education

Teachers and students are using generative AI for lesson planning, feedback, and language tutoring. Platforms like Khanmigo (built by Khan Academy using GPT-4) offer AI-powered learning assistants that explain concepts, quiz students, and help with homework, while keeping the teacher in control.

Educators are also using AI to generate rubrics, adapt materials for different learning levels, and create content faster, saving time without sacrificing quality.

Responsible AI: Why Ethics and Oversight Matter

As powerful as generative AI is, it’s not without risks. Left unchecked, these systems can amplify bias, spread misinformation, and produce outputs that are misleading or outright harmful. That’s why responsible AI isn’t optional, it’s essential.

Large models don’t “understand” what’s true or fair. They reflect the data they were trained on, which can include stereotypes, outdated assumptions, or biased patterns from the internet. According to a Harvard Berkman Klein Center report, addressing these issues requires both technical safeguards and human oversight.

Real-World Example: AI Resume Screening at Amazon

In 2018, Amazon shut down an internal AI recruiting tool after discovering it had developed a strong bias against female applicants. The model had been trained on historical hiring data, and because most past hires were men, the AI learned to rank male-coded resumes higher, even penalizing words like “women’s” (as in “women’s chess club”).

This wasn’t a malicious algorithm, it simply absorbed bias from the data it was fed. But it’s a clear example of what can happen when AI is used to make decisions without thoughtful guardrails.

Key Principles of Responsible AI

• Fairness: Avoid reinforcing harmful biases in training data or outputs.
• Transparency: Make it clear when AI is being used, and explain how decisions are made when possible.
• Privacy: Never feed sensitive or personal data into tools that store or learn from your input.
• Regulatory compliance: Follow frameworks like GDPR, HIPAA, and upcoming AI regulations in the EU and U.S.
• Human-in-the-loop: Use AI as a support tool, not an unchecked decision-maker.

Even as a casual user, there are ways to be more responsible. Double-check facts. Don’t paste sensitive data into third-party tools. Review content before publishing. Tools like AI Fairness 360 and Fairlearn offer ways to audit and improve fairness in more advanced systems.

Organizations like Partnership on AI and AI Now Institute are working to ensure AI is developed with ethics, safety, and long-term impact in mind.

AI can absolutely enhance our work, but it shouldn’t replace our judgment.

What’s Next for Generative AI: Key Trends to Watch

Generative AI is still in its early chapters, and the pace of innovation isn’t slowing down. As new models and tools emerge, here are some of the major trends shaping the future of this technology, and what they could mean for how we work, create, and communicate.

From Massive Models to Smaller, Smarter Ones

For years, the focus was on building bigger and more powerful models like GPT-4 or Claude 3. But now, there’s growing interest in smaller, task-specific models that are easier to train, faster to run, and more affordable to deploy. These models can be fine-tuned for customer service, legal research, tutoring, and more, without needing the resources of a tech giant.

Meta’s LLaMA 3 family and the rise of open-source alternatives show that lightweight, locally-run models could soon become standard across industries.

Multimodal Models

The next generation of AI doesn’t just handle text. It can understand and generate images, audio, and video, sometimes all at once. Tools like OpenAI’s Sora and Google’s Gemini 1.5 can already take in a combination of documents, images, charts, and spoken language to answer questions or create content.

This opens the door to applications like AI-powered tutoring with visuals, voice-based assistants that understand screenshots, or marketing tools that generate content across multiple formats.

Customization at Scale

We’re heading toward a world where people, not just corporations, will be able to fine-tune their own models. That means training a model on your writing style, your team’s knowledge base, or your company’s tone of voice.

Tools like Mistral 7B and platforms like Replicate are making this far more accessible. Instead of using one-size-fits-all models, we’ll see businesses spinning up customized AI agents to handle everything from onboarding to internal documentation.

Regulation and AI Governance

With great power comes increasing scrutiny. Governments are actively creating legal frameworks to regulate AI, especially in sensitive areas like healthcare, employment, surveillance, and education.

The European Union AI Act is one of the most comprehensive efforts so far, aiming to classify and regulate AI based on risk. The U.S. has issued executive orders, Japan is building out its AI safety standards, and over 70 national-level AI policies were introduced in 2023 alone, according to the Stanford 2024 AI Index.

If you’re building with AI, it’s no longer enough to think about what’s possible. You need to think about what’s permissible, and what’s responsible.

The AI-Augmented Workforce

AI isn’t just reshaping technology. It’s changing careers. According to the World Economic Forum’s 2023 Future of Jobs Report, 44 percent of workers’ core skills are expected to shift by 2028. Some repetitive roles may shrink, but demand is rising for people who understand how to use AI to boost their productivity.

Instead of asking “Will AI take my job?”, a better question is:
“How can I use AI to do my job better?”

In marketing, education, customer service, admin, and creative roles, AI is already acting as a support system, helping with brainstorming, summarization, content drafting, and automation. As Sequoia Capital put it, “The real unlock comes not from replacing humans, but from enhancing them.”

What matters now is AI fluency. Can you write effective prompts? Can you spot hallucinations? Can you integrate tools like ChatGPT into your daily workflow?

Those who can combine human skills like creativity, critical thinking, and emotional intelligence with AI tools are becoming the most valuable people in the room.

Wrapping Up: Where to Go From Here

Taking the Generative AI for Beginners course gave me a solid foundation, but more importantly, it sparked curiosity. Once you understand the basic terms and use cases, the fear starts to fade. You realize AI isn’t just for developers or data scientists. It’s for writers, marketers, teachers, solopreneurs, and anyone who wants to save time, create faster, or work smarter.

This blog barely scratches the surface, but if it helped demystify the landscape even a little, you’re already ahead of most people. The best thing you can do now? Keep experimenting. Try new tools. Ask better prompts. Build small automations. And stay informed, because this space is changing fast.

If you want to go deeper into learning how to code with AI, check out how to use ChatGPT to code with zero experience.

Generative AI isn’t coming. It’s already here, and it’s ready when you are.