A Comprehensive Guide to AI in 2025: From Beginner to Advanced |

Here is the cliffnotes version of everything you need to know about AI in 2025. We'll be going from beginner to intermediate to advanced, providing a crash course on each topic as well as providing more resources if you want to dig deeper into any of them. By the end of this article, you will know more about AI than 99% of the population, provided you retain the information.

The Structure of the Article

First, we're going to go over the basic definitions of AI and how they work. Then, we'll be covering prompting, followed by agents—a very hot topic these days—followed by AI-assisted coding. We're building applications through what is called vibe coding and finally looking at some emerging technologies going into the second half of 2025.

Understanding the Fundamentals of AI

Let's get started by first defining artificial intelligence. Artificial intelligence refers to computer programs that can complete cognitive tasks typically associated with human intelligence. Now, AI as a field has been around for a very long time. Some examples of traditional artificial intelligence, which back in the day we used to call machine learning, include things like Google search algorithms or YouTube's recommendation system.

But what we typically refer to as AI these days is what is called generative AI, which is a specific subset of artificial intelligence that can generate new content such as text, images, audio, video, and other types of media. The most popular example of a generative AI model is one that can process text and output text, otherwise known as a large language model or LLM.

Some examples of large language models include the GPT family from OpenAI, Gemini from Google, and the Claude models from Anthropic. These days, there are so many different types of models, and many models are also natively multimodal, which means that you can input and output not only text but also images, audio, and video. Your favorite models like GPD40 or Gemini 2.5 Pro are all multimodal.

Now that you know some of the basic key terms used in the AI world, let's move on to how to actually get the most out of these AI models through prompting.

Mastering the Art of Prompting

Let's first define prompting. Prompting is the process of providing specific instructions to a GenAI tool to receive new information or to achieve a desired outcome on a task. This can be through text, images, audio, video, or even code. Prompting is the single highest return-on-investment skill that you can possibly learn. It's also foundational for every other more advanced AI skill. This makes sense because prompting is how to communicate with these AI models. You can have the fanciest models, the fanciest tools, the fanciest whatever, but if you don't know how to interact with it, it's still useless.

To get started and practice prompting as a beginner, I have two mnemonics for you which, if you can remember and implement, will make you better at prompting than 98% of the population.

The "Tiny Crabs Ride Enormous Iguanas" Framework

The first one is what I call the Tiny Crabs Ride Enormous Iguanas framework, which stands for: - Task - Context - Resources - Evaluate - Iterate

When you are crafting a prompt, the first thing that you want to think of is the task. What do you want the AI to do? For example, maybe you want the AI to help you make some Instagram posts to market your new octopus merch line. You could just prompt it, "create an IG post marketing my new octopus merch line." With that, you'll probably get some okay results, but you can make the results much better.

First, you can add in a persona by telling the AI to act as an expert IG influencer. This allows the AI to take on the role of an IG influencer and use some of that more specific domain knowledge. Then, you can also add in the desired format of the output. Maybe you want something that's a little bit more structured. You can ask it to start the caption with a fun fact about octopi, then followed by the announcement and ending with three relevant hashtags.

The next part of this framework is context. The general rule of thumb is that the more context you can provide to the AI, the more specific and the better the results are going to be. The most obvious piece of context that we can provide right now is some information about the actual merch that we're selling. We can also add in some background about our company. For instance, our company is called Lonely Octopus, where we teach people AI skills. Some additional context we can give the AI is that our mascot, which is on the merch, is called Inky. We can also be more specific about our launch date and our target audience, like people between the ages of 20 to 40, mostly working professionals.

Next up is references. This is where you can provide examples of other IG posts that you like. This way, the AI can take inspiration from these examples. Providing examples can be so powerful because you can describe things with words as much as you like, but an example captures nuances that can be incorporated into the results.

Now, you want to evaluate. Do you like the result? Is there anything that you want to tweak or change? If so, you go into the final step of the framework, which is to iterate. Interacting with AI models is a very iterative process. If it doesn't get what you want the first time, you can tell it to tweak a little bit, add something, or change something, and you work alongside the AI to get the result that you finally want.

The "Ramen Saves Tragic Idiots" Framework

If you use the first framework and feel like the results are still not quite there, you can elevate this even further using the Ramen Saves Tragic Idiots framework.

Revisit: Revisit the "Tiny Crabs Ride Enormous Iguanas" framework. See if you can add something else, maybe a persona, be more detailed about the output, or add more references. Also, consider taking something out. Is there any conflicting information that could be confusing for the AI?
Separate: Separate the prompt into shorter sentences. Talking to AI is similar to talking to a human; a long, jumbled request can be confusing. Consider splitting what you're saying into shorter sentences to make it more clear and concise.
Try Different Phrasing: Try different phrasing and analogous tasks. For example, maybe you're asking AI to help you write a speech, and it's just not quite hitting the mark. You can reframe this. Instead of saying, "Help me write a speech," say instead, "Help me write a story illustrating my point." After all, what makes a good speech is a compelling and powerful story.
Introduce Constraints: If you feel like the output from your AI is just not quite there, you can consider introducing constraints to make the results more specific and targeted. For example, if you're making a playlist for a road trip across Texas and you're not quite vibing with it, you can introduce a constraint like, "only include country music suitable for the summertime."

With these two frameworks together, you'll be better than 98% of people at prompting. Especially for more advanced applications like building agents and coding, prompting is getting more important than ever. It's the glue that holds everything together to make sure that you get the results that you want consistently.

The Rise of AI Agents

AI agents are software systems that use AI to pursue goals and complete tasks on behalf of users. When we refer to AI agents, we usually refer to it as an AI version of a specific type of role.

For example, a customer service AI agent should be able to receive an email from somebody being like, "I forgot my password and I can't log in," and it should be able to reply to that email and reference the forgot password page on the website. As of today, it can't handle all of the queries that a customer service person would receive, but it can handle a lot of these generic or common questions autonomously.

Similarly, for a coding agent, if you prompt it well and you tell it to build a web application, it should be able to come back with an MVP version of that web application. You'll still have to add on a bunch of things and tweak it for sure, but it can write the code for the first version of it.

AI agents is a space where there's a lot of interest and a lot of money being poured into it, and they are expected to get better and better over time and incorporate into all sorts of products and businesses. In fact, a golden piece of advice about AI agents is that for every SaaS (Software as a Service) company, there will be a vertical AI agent version of it. For every company that is a SaaS unicorn, you could imagine there's a vertical AI unicorn equivalent.

The Components of an AI Agent

So what exactly makes up an AI agent? There are a lot of frameworks out there, but one of the best comes from OpenAI. They list six components that make up an AI agent:

The AI Model: You can't have an AI agent without a model. This is the engine that powers the reasoning and the decision-making capabilities of the AI agent.
Tools: By providing your AI agent with different types of tools, you allow it to interact with different interfaces and access different information. For example, you can give your AI agent an email tool where it's able to access your email account and send emails on your behalf.
Knowledge and Memory: You can give your agent access to a specific database about your company so that it's able to answer questions and analyze data specific to your company. Memory is also important for specific types of agents. For instance, a therapy agent should remember previous sessions.
Audio and Speech: This gives your AI agent the capability of interacting with you through natural language, allowing you to just talk to it in a variety of different languages.
Guardrails: It would be no good if your AI agent goes rogue. We have systems for that to make sure that your AI agent is kept in check.
Orchestration: These are processes that allow you to deploy your agent in specific environments, monitor them, and also improve them over time.

There is still a big gap between building AI demos and AI that actually does useful stuff in your business. Platforms are emerging that allow you to build apps that connect to your actual systems and take real actions. You can use any LLM, and your agents can actually read and write to your databases, not just chat with you. These platforms also have end-to-end support, including testing, monitoring, access control, and a lot more—all things that are not flashy but really crucial to real implementation in your business. Companies using these tools are already seeing genuinely impressive results. For example, some medical centers have increased their diagnostic capacity by over 10 times.

Building and Scaling Agents

Prompting is also really important when it comes to agents, especially if you're building multi-agent systems where you have networks of agents interacting with each other. Your prompts need to be very precise and produce consistent results.

So how do we actually build these AI agents? There are quite a few technologies currently available. - No-Code/Low-Code: Tools like n8n are great for general use cases, and others are excellent for enterprise use cases. - Coding: For those who can code, there are SDKs from major AI labs like OpenAI, Google, and Anthropic that have all these components built-in.

These different technologies and implementation methods are going to keep changing over time. That's why it's recommended to focus on the fundamental knowledge about the components of AI agents, because this foundational knowledge is not going to change so quickly and will be applicable to whatever new tool and technology comes out.

Often, you may also want to build multi-agent systems. The reason for this is that if you have one person trying to do everything in a company, it's probably not going to be great. It's much better to have people with specific roles. This is very similar with agents. It's often good to break a task down into different sub-agents that have specific roles and work together.

You may also have heard about MCP (Model-Component Protocol). It's basically a standardized way for your agents to have access to tools and knowledge. You can think about it like a universal USB plug. Prior to MCP, it was quite difficult to give agents access to certain tools because all the different websites and APIs do it in a different way. With MCP, you're now able to give your agents any type of tool and knowledge very easily, assuming it follows the MCP protocol.

Building Applications with Vibe Coding

Next up, let's move on to using AI to build applications, also known as AI-assisted coding or "vibe coding." In February of 2025, Andrej Karpathy, a co-founder of OpenAI, mentioned a new kind of coding he calls vibe coding, where you fully give into the vibes and forget that the code even exists. It's possible because the LLMs are getting so good. You simply tell the AI what it is that you want it to build, and it just handles the implementation for you. This is the new way of incorporating AI into your products and your workflows.

For example, you can simply tell an LLM: Please create for me a simple React web app called Daily Vibes. Users can select a mood from a list of emojis, optionally write a short note, and submit it. Below, show a list of past mood entries with a date and a note. And the LLM writes the code for you and generates the app. But it doesn't just end there. There are still skills, principles, and best practices for how to work with AI in order to vibe code properly and produce products that are actually usable and scalable.

A Framework for Vibe Coding

Here is a five-step framework for vibe coding with the mnemonic Tiny Ferrets Carry Dangerous Code: - Thinking - Frameworks - Checkpoints - Debugging - Context

Thinking: This is about thinking really hard about what it is that you actually want to build. The best way of doing this is to create a Product Requirements Document (PRD), where you define your target audience, your core features, and the technology you'll use.
Frameworks: Whatever it is that you're trying to build, similar things have probably been built before. It's much better to point the AI towards the correct tools for building your specific product by telling it to use React, Tailwind, or Three.js for 3D experiences. If you don't know what to use, you can ask the AI directly for common frameworks for your project type.
Checkpoints: Always use version control like Git or GitHub. Otherwise, things will break, and you will lose your progress.
Debugging: You are probably going to spend more time debugging and fixing your code than actually building anything new. Be methodical and patient, and guide the AI towards where it needs to fix things. The first place to start is to copy-paste the error message directly into the AI.
Context: Whenever you're in doubt, add more context. The more context you provide to AI—whether you're building, debugging, or doing whatever—the better the results are going to be. This includes providing the AI with mockups, examples, and screenshots.

Tools for Vibe Coding

There is a full spectrum of development tools available: - Beginner-Friendly: For those with no engineering background, popular tools include Lovable, V0, and Bolt. - Intermediate: A tool like Repl.it is still very beginner-friendly but also showcases the codebase so you can dig in a little more. Firebase Studio has both a user-friendly prompting mode and a full IDE experience built on VS Code. - Advanced: For more advanced work, there are AI code editors and coding agents like Windsurf and Cursor. Development is on your local machine, so the setup is more complex, but you have access to a full suite of development tools. - Expert: On the most advanced side of the spectrum, you have command-line tools like Claude Code. These tools live directly in your terminal and give you much more functionality. The expectation here is that you do really need to know how to code and have a deep understanding of software.

What's Next? Future Trends in AI

In the AI world, we don't measure things in terms of years or even months; we measure things in terms of weeks. The timelines are just getting more and more compressed. Dario Amodei, the CEO of Anthropic, made a good analogy: it's basically like being strapped on a rocket that is going through time, and time and space are warping so that everything is speeding up.

Because of this, if you're just trying to keep up with all the AI news, you will never be able to catch up. That's why the best advice is to not pay too much attention to all the new things coming out but instead focus on the underlying trends. There are three major underlying trends:

Integration into Workflows: 2025 is definitely the year in which people are taking AI and actually integrating it into their existing workflows. A prime example is Google integrating AI throughout its products. This should be a model for all companies: think about how to improve your processes by incorporating AI.
Productivity in Development: There's a massive productivity boost if you learn how to do AI-assisted coding. With a full spectrum of coding tools, there's a dramatic decrease in the barrier to entry for people who want to build things. There's also a big push towards increasing the productivity of developers, especially with command-line tools.
Focus on AI Agents: The focus on AI agents is not going away. In fact, there's more and more interest because agents have so much potential in improving existing products and for building new products. They allow experiences to be personalized, available 24/7, and at a much lower cost. As mentioned, for every SaaS unicorn company, there will probably be an equivalent AI agent company.

If you want to build something—a business, a startup, whatever—it is highly recommended to look into AI agents.