AI Explained Using Analogies

A Large Language Model (LLM) is like a massive, super-detailed map of human language created by observing countless written materials—books, websites, articles, conversations—over many years.

Imagine you have a gigantic atlas that doesn’t just show roads but also every language nuance: where words usually appear, how sentences tend to flow, what phrases mean in different contexts, and even common patterns of grammar and style. When you ask a question or give a prompt, the LLM is like a skilled navigator who looks across this vast map and predicts the most likely best path of words that form your answer. It’s not “understanding” the way humans do, but it’s incredibly good at guessing what word, phrase, or sentence should come next based on the patterns it learned.

The “large” in LLM means the model has billions or trillions of parameters—think of these as points on the map that help it remember patterns and relationships in language. The bigger the map, the more it can cover different topics, styles, and languages, and the better it gets at generating fluent, relevant text, answering questions, translating, summarizing, and even writing code.

But it’s still a map, not a traveler with experiences. It cannot feel meaning or truly understand context the way people do. Instead, it generates text by piecing together probable language sequences learned from its training data, much like tracing a path on a map by following known routes and landmarks.

So, LLMs are powerful language pattern predictors using a huge mental atlas of text but lack human-like comprehension or consciousness.

AI is not magic—it’s like the difference between a stage magician pulling a rabbit from a hat versus a factory that produces thousands of stuffed rabbits per hour.

When the magician pulls out the rabbit, it feels miraculous because you can’t see the mechanism. You don’t know about the hidden compartment, the misdirection, or the years of practice. The mystery creates the sense of magic.

But if someone takes you behind the curtain and shows you the false bottom in the hat, the secret pocket in the cape, and the exact hand movements that create the illusion, the magic disappears. It becomes a clever trick—impressive craftsmanship, sure—but completely explainable.

AI feels magical for the same reason: you can’t see the mechanism. When you type a question and get a sophisticated answer in seconds, it seems impossible, almost supernatural. But there’s no magic happening. It’s millions of mathematical calculations, pattern matching across enormous datasets, probability distributions selecting the most likely next word, and algorithms following rules written by human programmers.

The “wow” factor comes from three things: speed (billions of operations per second), scale (trained on more text than any human could read in lifetimes), and complexity (so many interconnected pieces that the whole system seems greater than its parts). But each individual piece is mundane—just math, code, and data.

Once you understand that AI is essentially very fast, very complex pattern-matching machinery built by humans using statistics and engineering, the mystical quality vanishes. It becomes what it actually is: an impressive tool with clear rules, limitations, and explanations for everything it does.

AI doesn’t think like humans—it’s more like the difference between someone who has lived in a city their entire life versus someone who has studied millions of photographs of that city from every possible angle but has never actually walked its streets.

The person who lived there can tell you, “Don’t go down that alley at night, it feels sketchy,” or “The best coffee shop isn’t the famous one, it’s the weird place next to the hardware store because the owner is passionate.” They have experience—they’ve felt cold mornings, smelled the bakery, gotten lost, learned from mistakes, changed their mind about neighborhoods.

The AI is like the photo-studier. When you ask it a question, it’s frantically cross-referencing millions of images to construct an answer that looks like what someone who’d been there would say. It can tell you “People often recommend avoiding that alley” because statistically, many photos showed that alley empty. It can suggest coffee shops that appeared frequently in food blogs. But it has never tasted coffee, felt uncomfortable, or genuinely preferred anything. It has no internal experience—no “aha!” moments, no gut feelings, no actual understanding of why that alley feels sketchy.

This is why AI can be confidently wrong—it’s assembling an answer that looks right based on patterns, the same way someone could describe a city remarkably well from photos alone, but then confidently tell you the bakery is on the wrong corner because that’s where it appeared most often in their image collection.

Predictive AI is like a master chef who has never tasted food but has spent years meticulously documenting every dinner party they’ve ever observed through a window.

They’ve recorded everything: when the host served salmon, 87% of guests smiled within 30 seconds. When the appetizer was cold, 62% of parties ended before 10 PM. When someone brought red wine to a pasta dinner, the host invited them back 91% of the time. The chef has millions of these observations, cross-referenced by season, guest demographics, table arrangements, everything.

Now someone asks this chef: “Should I serve chicken or beef at my anniversary dinner in December for 8 people?” The chef doesn’t actually know what tastes good—they’ve never experienced flavor. But they scan through all their observations and see that in similar scenarios (December, 8 guests, anniversary context), beef appeared in 73% of successful parties while chicken appeared in 45%. So they confidently predict: “Serve beef.”

Here’s the twist: They’re making this recommendation based purely on correlation patterns in past observations, not because they understand why beef might be better. Maybe those beef dinners succeeded because the hosts were better cooks, or the guests happened to be in good moods, or beef was simply trendier that year. The predictive AI has found a statistical pattern and is betting on it repeating—but it has zero understanding of the actual cause-and-effect relationship.

This is why predictive AI can be incredibly accurate in stable environments where patterns hold true, but spectacularly wrong when circumstances shift in ways the historical data never captured.

Tokenization is like how your brain processes a song streaming through your headphones. You don’t actually hear the entire 3-minute song as one continuous sound blob—your brain automatically chunks it into measures, beats, phrases, verses, and choruses. Some musical sections are long and sweeping, others are short staccato bursts.

When the AI reads your text, it does something similar. It can’t swallow “Please write me a comprehensive analysis” as one giant unit. Instead, it breaks it into rhythm patterns: “Please” might be one beat, “write” another, but “comprehensive” gets split into “compre-hen-sive” because it’s a longer, more complex pattern. Common words like “the” are single, familiar beats the AI recognizes instantly, while rare or made-up words get broken into smaller sound patterns it can identify.

Here’s the crucial part: Just like your phone can only display a certain number of songs in your “Recently Played” list before older ones scroll off, AI has a limit to how many of these text-beats it can hold in its working memory at once. This is why you sometimes hit token limits—the AI’s “playlist” is full, and it needs you to stop adding more tracks before it can process what you’re asking.

The wild part? Different languages and writing styles create different rhythmic patterns, which is why the same sentence might use more tokens in one language than another.

A context window is like having a conversation through a moving train window with someone standing on the platform.

Imagine the train is slowly rolling through the station. At first, when the conversation starts, you’re right next to each other—you can see their face clearly, remember everything they said, reference their jacket color, recall their first question. But as the train keeps moving, that person stays in place on the platform while you drift further away.

The context window is how far you can still see and hear them clearly. Maybe you can still make out the last five minutes of conversation, still reference what they said about their dog, still remember the question they asked three exchanges ago. But eventually, the train moves far enough that the beginning of the conversation disappears into the distance—you literally can’t see or hear those early moments anymore. If they ask “So going back to what I said when I first walked up,” you’re stuck because that part of the platform is now too far behind you.

Some AI models have tiny train windows (you lose sight quickly), while others have panoramic observation car windows (you can see much further back). But every AI has a limit—eventually, the start of the conversation vanishes beyond the window’s edge, and the AI genuinely cannot see it anymore.

Prompt engineering is like the difference between asking a five-year-old to “clean your room” versus giving them specific instructions: “Put all the dirty clothes in the hamper, stack your books on the shelf by size, and put toys in the blue bin.”

The five-year-old has the capability to clean. They physically can do it. But “clean your room” is so vague that they might just shove everything under the bed and call it done. They’re not being defiant—they genuinely interpreted your request based on their understanding of “clean,” which is different from yours.

When you get specific with instructions, suddenly the same kid with the same abilities produces dramatically different results. You didn’t make them smarter or give them new skills. You just communicated in a way that aligned with how they process instructions.

AI is that five-year-old. It has enormous capabilities, but it’s wildly literal and has no intuition about what you “really meant” or “obviously wanted.” When you type “write me an email,” it has a million possible interpretations—formal or casual? Long or short? To whom? About what? So it makes its best statistical guess and gives you something generic.

But when you prompt it with “Write a 3-paragraph formal email to my department chair requesting a meeting next Tuesday to discuss my research budget, using a respectful but assertive tone,” you’re suddenly getting much closer to what you actually wanted. Same AI, same capabilities—you just learned to speak its language.

Prompt engineering isn’t about tricking the AI or finding magic words. It’s about learning to communicate with something that takes everything literally and needs more context than a human would.

AI hallucinations are like when you’re trying to remember your childhood friend’s birthday party, but you weren’t actually there—you only heard your older sister describe it years later in bits and pieces.

When someone asks you “What kind of cake did they have?” your brain doesn’t say “I don’t know, I wasn’t there.” Instead, it frantically assembles fragments: your sister mentioned chocolate once, most birthday parties have chocolate cake, the friend’s mom made great desserts. So you confidently answer: “It was a chocolate layer cake with buttercream frosting.”

But here’s the thing—you’re not lying. You’re not guessing randomly. Your brain genuinely constructed what feels like a memory based on plausible patterns. The confidence you feel when you say it is completely real, even though the “memory” is fabricated from statistical likelihood rather than actual experience.

This is exactly what AI does when it hallucinates. It’s not malfunctioning or intentionally making things up. It’s doing what it was designed to do: generating the most statistically probable continuation of text based on patterns it learned. When you ask it for a citation or a specific fact it doesn’t actually have stored, it doesn’t experience “I don’t know” the way humans do. Instead, it constructs something that looks right based on the patterns of how citations appear, how facts are structured, how academic references are formatted.

RAG (Retrieval-Augmented Generation) is like the difference between a trivia contestant who has to answer questions from memory alone versus one who’s allowed to quickly flip through a reference book before answering.

Imagine you’re on a game show. The regular AI contestant has spent years studying and memorizing facts, but once the show starts, they’re locked in a soundproof booth with only their memory. When the host asks “What’s the company’s return policy for items purchased in the last 30 days?” the contestant has to construct an answer based on general patterns they remember about return policies—but they’ve never actually seen THIS company’s current policy document.

Now imagine the RAG contestant gets a different setup. When the question is asked, a runner immediately sprints to a filing cabinet, pulls out the exact current policy document, highlights the relevant section about 30-day returns, and slides it through a slot into the booth. The contestant reads that specific, up-to-date information and then crafts their answer using both their general knowledge about how to communicate policies AND the precise details from the actual document.

The magic is in that sprint to the filing cabinet. RAG doesn’t make the AI smarter—it gives it access to a library card. The AI still does the thinking and writing, but now it’s working with verified, current information rather than trying to reconstruct facts from fuzzy training memories.

This is why RAG-powered AI can answer questions about your company’s internal documents or last week’s policy changes—it’s literally looking them up in real-time before responding.

Agentic AI is like the difference between a personal assistant who waits for you to tell them every single thing to do versus an executive assistant who actually manages your life.

The regular assistant stands there with a notepad. You say “Schedule a meeting with Susan,” and they schedule it. You say “Find me flights to Boston,” and they find flights. Every single action requires you to stop what you’re doing, think about what needs to happen next, and give an explicit instruction. They’re incredibly helpful, but you’re still doing all the thinking and planning—they’re just the hands executing your commands.

The executive assistant is completely different. You tell them once: “I need to be in Boston next Thursday for a conference.” Then they disappear and start doing things. They check your calendar and find conflicts. They reach out to those conflicting meetings to reschedule them. They book your flight, but they also notice you have an early morning session so they book an evening flight the day before. They reserve a hotel near the conference venue. They add the conference location to your calendar with travel time buffers. They even notice you’re speaking on a panel and proactively send you the moderator’s bio and recent panel topics. You didn’t ask for any of that—they understood the goal and autonomously figured out all the steps needed to make it happen.

That’s agentic AI. It doesn’t wait for step-by-step instructions. You give it a goal, and it independently figures out the plan, adapts when things change, and keeps working until the objective is achieved.

When AI companies say “your data isn’t used to train our AI,” it’s like a hotel promising “We don’t sell your room key to other guests.”

Okay, great—but that’s addressing one very specific concern while conveniently sidestepping a dozen others. What about the security camera footage of you in the lobby? The record of every time you used your key card? The list of everything you ordered from room service? The data about what time you left and returned each day? The information about which websites you visited on the hotel WiFi?

The hotel technically kept their promise—they didn’t sell your physical room key. But they never said anything about all the other data trails you left behind during your stay.

This is exactly what’s happening with AI companies. When they say they don’t use your conversations for training, they’re answering one narrow question while dozens of other data uses remain unaddressed:

They might store your conversations indefinitely “for quality assurance.” Human reviewers might read your private prompts to improve the service. Your data might be analyzed for business insights. It could be shared with third-party partners under vague terms. The company might use it to improve other products you never consented to. Your usage patterns might inform decisions about features or pricing.

The real issue is that “not used for training” has become a carefully crafted reassurance that sounds comprehensive but is actually incredibly narrow. It’s designed to make you stop asking questions, when the honest answer would require them to disclose a much longer, less comfortable list of what they actually DO with your data.

Always ask the follow-up question: “Okay, so what do you use my data for?”

Accusing someone of using AI to write something is like being a food critic who insists they can tell if a dish was made from scratch or used a boxed mix—based entirely on vibes.

Sometimes they’re right. The cake is suspiciously perfect, with uniform texture and that telltale processed sweetness. The frosting has no personality quirks, no evidence of someone’s grandmother’s secret technique. It tastes like it came from a formula, not a kitchen with history.

But sometimes they’re spectacularly wrong. Maybe the baker is just really good at consistent technique. Maybe they trained at culinary school and learned to eliminate the “imperfections” that signal homemade. Or maybe the baker is a non-native cook who learned from cookbooks, so their style naturally reads as more formal and structured than someone who grew up improvising in their family kitchen.

The accusation itself creates this awkward moment where the accused has to defend their authenticity. “No really, I made this myself!” But how do you prove it? Show your messy kitchen? Recreate the dish while someone watches? The burden of proof shifts unfairly to the person being accused, and even if they’re innocent, the suspicion lingers.

The really tricky part is that we’re entering an era where the “boxed mix” is so good that even experts can’t reliably tell the difference—and plenty of excellent bakers are now using shortcuts as tools, combining their skills with convenience products to work faster. So the accusation becomes less about actual cheating and more about gatekeeping an imaginary standard of “authentic” creation.

AGI (Artificial General Intelligence) is like the difference between someone who has memorized the entire scripts of every TV medical drama versus an actual doctor who went to medical school.

The script-memorizer is incredible at what they do. Ask them about any medical situation that’s ever appeared on TV and they can rattle off exact dialogue, procedure names, dramatic turning points. They can even generate new, plausible-sounding medical scenarios by remixing patterns from thousands of episodes. Put them in a writers’ room for a medical show and they’d be brilliant. They might even fool you into thinking they understand medicine.

But take them into a real emergency room with an actual patient, and everything falls apart. They can’t adapt when the patient’s symptoms don’t match a TV plot. They don’t actually understand why treatments work or how the human body functions. They can’t improvise when the textbook case goes sideways. They’ve never experienced the fundamental reasoning that connects “this symptom” to “this underlying cause” to “this intervention”—they’ve only seen the surface patterns of how those connections appear in stories.

AGI would be the actual doctor—someone who doesn’t just pattern-match from millions of examples but truly understands the underlying principles, can reason from first principles, learn entirely new specialties, and adapt to situations they’ve never encountered before.

We haven’t achieved AGI because we’ve gotten extraordinarily good at building the script-memorizers, but we have no idea how to build the actual doctor. We don’t know how understanding works, how true reasoning emerges, or how to create a system that genuinely grasps cause and effect rather than just statistical correlations. It’s like knowing how to build increasingly sophisticated puppets without knowing how to create actual life.

Think of Claude, Gemini, ChatGPT, and Perplexity as four very different types of expert guides you could hire for a grand, sprawling knowledge expedition:

<strong>Claude → The thoughtful, safety-conscious mountain guide.</strong> He listens carefully, double-checks your route, and prefers giving you a well-lit path rather than throwing you into the fog. He won’t take you somewhere if there’s a risk you’ll misread the terrain.

<strong>Gemini → The fast-moving multilingual world traveler.</strong> It darts across continents, switching languages and fields with ease, mixing real-time news with old history, but sometimes in the rush it hands you too many souvenirs at once.

<strong>ChatGPT → The friendly librarian with a magic storytelling pen.</strong> It’s great at drafting narratives and can role-play with you to help you learn, but sometimes adds a flourish that didn’t literally happen.

<strong>Perplexity → The detective-reporter with footnotes in hand.</strong> He not only finds the answer—he shows you where the evidence came from, citing sources directly so you can verify them.

If these four met at a café, Claude would make sure the group discussed ideas ethically and safely, Gemini would keep everyone on schedule while sharing global trivia, ChatGPT would narrate the meeting like a novel, and Perplexity would hand you a list of every reference used in the conversation.

Imagine you have two master chefs: ChatGPT and Gemini. Both have access to the same pantry (knowledge).

<strong>Custom GPTs (ChatGPT)</strong> are like personalized recipe books. They tell the chef exactly how you want dishes prepared—what flavors to emphasize, what restrictions to follow. Each Custom GPT recipe customizes the chef’s cooking so every meal matches your unique taste.

<strong>Gems (Google Gemini)</strong> are like magic cooking gadgets you hand the chef. These gadgets aren’t recipes but tools that instantly transform how Gemini cooks—maybe a tool for legal precision, or another for creative storytelling. With Gems, the Gemini chef adapts on the fly.

So, Custom GPTs tailor the recipe itself, while Gems give the chef specialized tools to change cooking techniques in real time.

Parameters in a Large Language Model (LLM) are like the tiny knobs and switches inside a colossal, intricate radio receiver that tune how it interprets and generates sound.

Imagine the LLM as an enormous machine with billions or even trillions of these “knobs”—each one controls subtle aspects of how the model transforms input words into output text. When the AI “learns” during training, what it’s really doing is adjusting these vast numbers of knobs to minimize errors in predicting the next word in a sentence across massive amounts of text.

Each parameter doesn’t represent a specific word or fact, but rather a tiny mathematical weight that influences how signals flow through the model’s neural network layers. The collective tuning of all these parameters enables the model to capture complex patterns in language, grammar, meaning, and style.

So parameters are the microscopic settings inside the AI’s brain that together orchestrate its ability to predict and generate language.

GPUs (Graphics Processing Units) are the core hardware engines powering large language models (LLMs). Think of them as supercharged engines that handle the heavy lifting of mathematical calculations needed for training and running these models.

Key functions include: Memory Capacity (VRAM) to store model weights, Processing Power with thousands of cores for parallel processing, and Specialized Hardware like NVIDIA’s Tensor Cores to accelerate calculations.

In essence, GPUs are the muscle behind AI’s big breakthroughs—enabling models to learn, adapt, and generate at speeds and scales that are impossible with traditional CPUs alone.

AI benchmarks are like the report cards for large language models (LLMs) and other AI systems—they are standardized tests that measure how well an AI performs across different tasks, helping researchers understand strengths, weaknesses, and progress.

Imagine giving a student a variety of exams: a math test, a reading comprehension quiz, an essay writing assignment, and a science problem-solving challenge. Each exam uses different criteria to score performance like correctness, depth, clarity, and creativity. AI benchmarks do the same but with language tasks like answering questions, summarizing texts, translating languages, coding, reasoning through problems, and more.

Some common AI benchmark metrics include: Accuracy, Completeness, Conciseness, Hallucination rate, Coherence, and Diversity.

Popular benchmark suites like GLUE and SuperGLUE test general language understanding, while MMLU assesses knowledge and reasoning across subjects like history, law, and science. Human evaluation often complements automated metrics to judge qualities like relevance and helpfulness.

Benchmarks are essential for AI developers to objectively compare models, track improvements, and identify areas needing work, similar to teachers using exams to decide which subjects need more focus before final exams.

The MCP Protocol developed by Claude AI, known as the Model Context Protocol (MCP), is like a universal connector or ‘USB-C port’ for AI systems. It standardizes how large language models (LLMs) like Claude connect with external tools, data sources, and services in real-time to expand their capabilities beyond what’s in their training data.

Imagine an AI agent wanting to use a browser, database, calendar, or financial service to answer a user’s query. MCP defines a common language and set of rules for this communication, enabling seamless interoperability between AI models and a wide range of external systems. This helps AI agents become “agentic,” able to plan, act, and adapt by interacting with the web and software tools dynamically.

Put simply, MCP is the plumbing and protocol that lets AI models like Claude safely plug into real-world data and tools, turning them into versatile, interactive assistants rather than isolated text generators.