How LLMs Understand Natural Language
Language is messy. The same sentence can mean different things depending on context, tone, or who's speaking. Words have multiple meanings, sarcasm flips a sentence's intent entirely, and pronouns can point to almost anything nearby. Yet LLMs handle this messiness remarkably...
My GenAI Blogs
Why GenAI? Generative AI has completely changed how I think about software, creativity, and problem-solving. Over the past year, I've gone deep into the world of large language models, prompt engineering, retrieval-augmented generation, fine-tuning, and AI agents. The pace of...
What Makes an LLM "Intelligent"?
Large language models can pass bar exams, write working code, debug their own mistakes, and explain complex topics in plain English. It's natural to call that "intelligent." But is it the same kind of intelligence humans have — or something else entirely wearing a very...
Foundation Models Explained
You've probably heard GPT-4, Claude, Gemini, and Llama described as "foundation models." It's become one of the defining terms of the current AI era — but what actually makes a model a "foundation" model, rather than just... a model? Here's what the term means and why it...
Training vs Inference in LLMs
Every large language model has two very different lives: the phase where it learns, and the phase where it works. These are called training and inference, and while they're related, they're almost opposite in how they operate, what they cost, and what they're trying to...
What is an AI Inference?
You'll often hear phrases like "inference costs," "running inference," or "inference speed" when people talk about deploying AI models. It's one of those terms that sounds technical but describes something fairly simple: the moment an AI model actually gets used to produce an...
Why LLMs Hallucinate
Ask an AI model a question and it might give you a confident, well-written, completely wrong answer — a fabricated statistic, a made-up book title, a court case that never existed. This phenomenon is called "hallucination," and it's one of the most important limitations to...
Prompt vs Completion
If you've worked with any AI language model — through an app, an API, or a chatbot — you've encountered these two terms, even if you didn't know their formal names. Every interaction with an LLM boils down to two halves: what you send in, and what the model sends back. Understanding …
Temperature and Top-P Explained
If you've ever used an AI API or poked around in a chatbot's advanced settings, you've probably seen sliders labeled "temperature" and "top-p." They sound technical and a little mysterious, but they control something fairly intuitive: how creative, random, or predictable a...
Parameters in Large Language Models
You've probably seen headlines like "GPT-4 has over a trillion parameters" or "this model has 7 billion parameters." But what actually is a parameter, and why does the number matter so much when people talk about AI capability? Let's break it down. What Is a Parameter,...
Tokens: The Building Blocks of LLMs (Part 2)
Every time you type a message to ChatGPT or Claude, something happens before the model even starts "thinking": your text gets chopped up into small pieces called tokens. Tokens are the fundamental units that large language models actually read, process, and generate — and...
How ChatGPT-Style Models Generate Text
Ever wonder what's actually happening between the moment you hit "enter" on a prompt and the moment text starts appearing on your screen? It's not retrieval, and it's not magic — it's a very fast, very structured process of prediction happening one small piece at a time....
Tokens: The Building Blocks of LLMs
If you've ever noticed that AI pricing is measured in "tokens," or seen a model get confused by an unusual word, you've bumped into one of the most fundamental — and least discussed — parts of how LLMs work. Before a model can predict, reason, or write a single sentence, it first …
What is an LLM?
An LLM, or Large Language Model, is a type of artificial intelligence trained to understand and generate human language. It's the technology behind tools like ChatGPT, Claude, and Gemini — the systems that can answer questions, write essays, summarize documents, translate...
How Large Language Models Work
Large Language Models (LLMs) like GPT-4, Claude, and Gemini can write essays, debug code, and hold surprisingly natural conversations. But under the hood, they're not "thinking" the way humans do — they're performing an extraordinarily sophisticated version of pattern...
Generative AI vs Traditional AI
Artificial intelligence isn't one single thing — it's an umbrella term covering very different kinds of systems built for very different jobs. Two terms you'll hear constantly today are "traditional AI" and "generative AI." They're related, but they solve fundamentally...
What is Generative AI?
If you've typed a prompt into ChatGPT, asked Midjourney to paint a dragon riding a bicycle, or had an AI write code for you, you've used generative AI. But what actually makes it "generative," and how is it different from the AI that's been around for years? The Short Answer …