Artificial intelligence is already reshaping how we work, learn, create, and connect. But understanding AI can still feel like a moving target that you started chasing a bit too late. There’s unfamiliar jargon, the breakthroughs are fast and frequent, and the distinctions between terms are confusing.
A few weeks ago, we published an AI glossary of terms for anyone who is brand new to AI. The intention was to level-set so everyone’s speaking the same language, and no one feels left behind on the basics.
This post is like a part two to that list. It lists core concepts you should understand to continue advancing your AI knowledge so you don’t just stay conversant in AI, but have the context to be strategic about its use.
Whether you’re a curious professional, an executive seeking clarity, or just someone tired of nodding through AI conversations you only half-understand, this guide is for you.
Here we go.
Intelligence is often described as the ability to solve problems or perform tasks in a way that seems “smart.” Learning, on the other hand, involves acquiring new knowledge or refining existing skills based on experience.
Early expert systems in the 1970s and 1980s showed how computers could look “intelligent” by relying on human-crafted rules. Yet these systems couldn't adapt on their own since they had no mechanism to learn from new data.
Think of an expert system like a digital recipe book: It follows instructions meticulously but can't spontaneously create new dishes. Today’s AI, by contrast, updates its internal parameters (or “weights”) as it learns, making it more flexible and adaptable.
Understanding the difference between intelligence (performing well) and learning (improving over time) helps us see why AI models are revolutionizing everything from business to entertainment. In the past, “expert” machines simply repeated fixed rules, lacking real adaptability. Now, AI systems can adjust in response to data. This makes them powerful tools for tasks ranging from customer support chatbots to warehouse robots.
A neural network is a set of digital “neurons” connected in layers, inspired by the human brain’s structure. It identifies patterns in data by adjusting numerical “weights,” improving accuracy with practice. For example, a neural network can spot fraudulent credit card transactions by detecting unusual spending patterns.
If you know anything about natural language processing, you might have also heard about transformer architecture. Transformers are a special type of neural network particularly good at processing language. Instead of reading text one word at a time, they consider the entire context all at once, leading to powerful language understanding. An AI helpdesk agent that instantly analyzes a user’s query, scanning company FAQs to generate relevant answers, is an example of where something like this comes into play.
GANs pit two models against each other—a “generator” that tries to produce realistic data and a “discriminator” that evaluates its authenticity. Over many rounds, the generator improves at producing convincing fakes (like realistic-looking images), and the discriminator gets better at spotting them. So a design team could use a GAN to brainstorm fashion concepts by generating novel clothing patterns. One could create, the other could critique. Ian Goodfellow, who introduced GANs, remains a key figure in generative modeling if you’d like to learn more from an expert.
RL is like training a puppy. You reward desired actions and discourage bad ones. An AI agent learns the best sequence of moves or decisions to maximize a reward in an environment. An example of this in action is a warehouse robot that might “earn points” each time it places a package on the right shelf, making it more efficient over time.
RLHF blends reinforcement learning with direct input from humans. Instead of only receiving automatic rewards, the AI also gets real-time signals from people, helping it refine its understanding of what “good” behavior looks like. A virtual customer service agent that learns to avoid annoying or repetitive responses because beta testers give it thumbs down is a practical example of this. This approach can guide AI in nuanced tasks, where feedback from humans is crucial for quality.
Fine tuning takes a pre-trained model—think of it like plain cookie dough—and adapts it for a specific purpose. With relatively little data, you can achieve high performance in a specialized task without training from scratch. A business might use a general speech model fine tuned with call center transcripts to better understand customer complaints, for example. Open source groups often release pre-trained models that anyone can fine tune for unique applications.
Distillation involves transferring the “know-how” from a powerful, resource-heavy “teacher” model into a smaller “student” model. Traditionally, this might mean matching the teacher’s outputs—like probability distributions or predicted responses—so that the student can mimic the teacher’s behavior with far fewer parameters.
Focusing on Anthropic’s Claude
Anthropic has explored a process where prompts and responses generated by a large model (Claude) are distilled into a more compact version. Instead of just copying raw probabilities, Anthropic’s method can incorporate entire prompt-response pairs. By feeding these back into the training process, the resulting smaller model embodies much of Claude’s capabilities but runs faster and requires fewer resources.
A big model (Claude) might generate detailed explanations for customer queries. Those prompt-response pairs become training data for a scaled-down model specialized in handling only one category—like retail refunds—which can then be embedded directly into a store’s customer service platform.
This is useful because you retain much of the teacher model’s insight without paying the full computational cost, and you can create specialized variants for different tasks while drawing on the original AI’s high-level knowledge.
Open models share code, data, and architecture details, encouraging community improvements and transparency. Closed models keep most information private, often to protect trade secrets or to ensure consistent performance under a company’s control.
Using an open source model lets startups customize AI for niche markets, while a closed model might lock you into a one-size-fits-all solution. Many open source communities (like developers on GitHub) encourage collaboration, while companies may maintain closed models for proprietary benefits.
Why do so many models exist? Different tasks—from image recognition to speech translation—require different AI architectures. Competition also drives innovation, resulting in diverse approaches, some open and some proprietary. Specialized models often outperform general purpose ones on specific tasks.
Model weights are the numeric values a neural network adjusts to minimize errors, making them the core “knowledge” of an AI system. They essentially store patterns learned from training data and determine how the AI transforms inputs (like words) into outputs (like answers or recommendations).
Some experts argue these weights are, in many ways, the true embodiment of an AI’s intelligence. They define the specific decisions and insights the model can produce. If you copy and share the weights, you’re effectively duplicating the AI’s capabilities.
Because of this, there have been proposals to regulate or discourage the free distribution of model weights. These proposals reflect concerns about misuse or unauthorized replication of powerful AI models.
Changing these weights over time is how AI learns. The precise values of these weights—and who has access to them—can significantly impact both innovation and oversight in the AI world.
Using One Model to Train Another
Sometimes a large, powerful model serves as a “teacher” by generating input-output examples that train a smaller “student” model. This approach can create specialized systems without needing massive human-labeled data.
As an example, a large language model could answer thousands of niche finance questions. Those Q&A pairs become a custom dataset to train a smaller, specialized model for handling routine banking support queries.
Hyperparameters are the “settings” you choose before training an AI model, like how many layers the network should have or the learning rate for weight updates. Tweaking them can dramatically change how fast and how well the model learns.
Think of it like adjusting your oven temperature and baking time. You can ruin or perfect your cookies based on these choices.
Many data scientists experiment heavily with hyperparameters to boost model performance.
Chain-of-Thought (CoT) is a method that lets a model outline its reasoning steps before arriving at a final answer. Instead of giving a one-shot response, the AI discloses its intermediate thinking (internally or externally). When done publicly, it’s like showing your math work on a test. This can help developers or users understand how the AI arrived at a particular conclusion.
A finance chatbot, for example, might detail its thought process when comparing two loan products—breaking down interest rates, repayment schedules, and hidden fees. This transparency helps build trust with users.
This is important for a few reasons:
Single shot prompting means providing one detailed example or a single input-output pair to guide the AI’s response. You don’t give the AI multiple examples—just one scenario to illustrate the pattern you want.
It’s like showing a friend how to do a new card trick by performing it once, then asking them to replicate it. This contrasts with “few-shot” prompting, where you provide multiple examples, and “zero-shot,” where you provide no examples (just instructions).
A product manager might say, “Here’s how I want this Q&A format to look,” and show one perfect example of a question and an answer. The AI then uses this single demonstration as a template.
One strong example can be enough for straightforward tasks, and this type of prompting ensures the AI follows a specific style or structure you demonstrate.
AGI is a good one to end on, since it represents the future of AI that many people are talking about. AI is moving from narrowly focused tools toward the grand idea of artificial general intelligence, where machines learn and adapt across countless tasks. Some experts believe emerging research will reveal new ways for AI to combine flexible reasoning, creativity, and self-directed learning.
Though we’re not there yet, the seeds of AGI can be seen in today’s large language models, reinforcement learning techniques, and collaborative human-in-the-loop approaches. They show how intelligence can evolve from multiple components working together.
As algorithms become more adaptive, transparent, and ethically guided, the path to AGI could shift from science fiction to reality. Some folks feel AGI is just around the corner, while others believe it will never happen.
The concepts we’ve explored here are more than definitions; they’re lenses that help you interpret what AI is doing, why it matters, and how you might use it.
Whether you’re charting a path for your business, collaborating with technical teams, or just building your own literacy, knowing the difference between a transformer and a GAN, or why model weights and hyperparameters matter, gives you a seat at the table. It means you’re no longer on the sidelines of the AI conversation—you’re part of it.
Of course, this is just the beginning. The field of AI is advancing quickly, but with the right foundational knowledge, you won’t just keep up—you’ll be better prepared to lead, question, and contribute thoughtfully.
So keep asking questions. Keep exploring. And remember that understanding AI isn’t about catching up. It’s about staying curious.