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Clear, beginner-friendly explanations of the concepts and terms you need to understand AI. We've ordered these from the basics on up, so reading in order is the easiest path in — but feel free to jump straight to whichever term you're curious about.
33 explainers
The benchmark scores—MMLU, GPQA, SWE-bench—that accompany every new AI model announcement. What they actually measure and how far you can trust them, explained for decision-makers through the contents of major benchmarks and the pitfalls of saturation and data contamination.
The three major paradigms of machine learning—supervised learning that's taught the answers, unsupervised learning that finds structure on its own, and reinforcement learning that learns from trial and reward—explained with concrete examples like classification, clustering, and AlphaGo, plus a comparison table.
What's the difference between AI, machine learning, and deep learning? For non-specialists: the three form a nested 'AI ⊃ machine learning ⊃ deep learning' structure. This explainer covers what each means, the idea of 'features' that separates machine learning from deep learning, and where generative AI and LLMs fit—drawing on Nature's deep learning review.
What does the '7B', '70B', or '405B' in an LLM's name mean? An explainer—based on the scaling-laws paper and technical sources—of parameter count: that B means a billion, why more tends to mean smarter and where that breaks down, the memory and GPU trade-off, and how quantization lightens the load, for non-specialists.
What exactly is the LLM (large language model) inside ChatGPT and Claude? How it learns language from vast text and generates output one token at a time, the Transformer turning point, the path from pretraining to fine-tuning and RLHF, and what LLMs can and cannot do—explained from official documentation.
Tokens appear in every LLM pricing table and limit spec — but what are they? This guide explains how AI breaks text into fragments, why Japanese tends to consume more tokens than English, and how pricing, context windows, and rate limits all count in tokens.
Generative AI sometimes states falsehoods with full confidence — a phenomenon called hallucination. Drawing on an OpenAI research paper and Anthropic's official documentation, this guide explains why it happens, what users can do in their prompts, and how developers mitigate it with RAG.
A plain-language guide to the context window—how much text an LLM can handle at once—based on official Anthropic and Google documentation: what a token is, why bigger isn't always better (context rot), and how to live with the limits.
What are 'temperature' and 'top-p,' the settings you always see in the ChatGPT and Claude APIs? We explain how AI picks the next word from a probability distribution—greedy decoding, temperature, top-k, and top-p (nucleus sampling)—with diagrams, for non-specialists.
When an AI 'learns,' what is it actually doing? We explain how a neural network adjusts its weights using three tools—the loss function, gradient descent (the descending-a-mountain analogy), and backpropagation—with diagrams, for non-specialists.
Why does AI get smarter as you add more parameters, data, and compute? We explain scaling laws—where loss falls along a power law—covering the Kaplan (2020) vs. Chinchilla (2022) clash and the debate over emergent abilities, with diagrams for non-specialists.
What is overfitting? A plain-language guide to how AI fits training data too closely and fails on new data—covering the difference from underfitting, the bias-variance tradeoff, train/validation/test splits, countermeasures like regularization, early stopping, and dropout, and the double descent observed in large models.
What is a GAN (Generative Adversarial Network)? A plain-language guide to how two AIs—a generator and a discriminator—are trained by competing like 'counterfeiters and police,' covering the minimax game, mode collapse, deepfakes, and how GANs differ from today's dominant diffusion models.
What is a neural network? From its building blocks—neurons, weights, activation functions, and layers—to how it learns through backpropagation and gradient descent, plus the history of the perceptron's limits and revival. A clear, illustrated guide to the foundation of generative AI.
What are zero-shot and few-shot learning? An explainer—based on OpenAI's GPT-3 paper—of 'in-context learning,' where you make an AI handle a new task by showing it zero or a few examples in the prompt without retraining the model, plus how it differs from fine-tuning.
What is chain-of-thought (CoT) prompting? An explainer—based on Google's CoT paper and the 'Let's think step by step' zero-shot CoT research—of why making an AI write out its intermediate reasoning sharply raises its accuracy on complex problems, and how CoT relates to reasoning models and prompt engineering.
What is MoE (Mixture of Experts)? An explainer—based on the original paper and technical sources—of the architecture behind modern large-scale AI, where total parameters are huge yet inference stays fast. Covers how it differs from dense models, how the router picks experts, and adoption in Mixtral, Gemini, and more, for non-specialists.
What is RLHF (Reinforcement Learning from Human Feedback)? An explainer—based on OpenAI's InstructGPT paper and other sources—of how a merely pre-trained LLM is shaped into an AI that responds helpfully and naturally, why ChatGPT and Claude feel so natural, and how RLHF relates to AI safety.
Compression makes giant AI models small and fast enough to run on phones and edge devices. Learn the difference between knowledge distillation (from teacher to student) and quantization (lowering numeric precision), and when to use each—explained for non-experts using Hinton's original paper and other sources.
OpenAI's o3, Claude's extended thinking, Gemini Deep Think—AI models that think carefully before answering are called reasoning models. Learn about their starting point, Chain-of-Thought prompting, how they allocate tokens to thinking, and the trade-off between capability, speed, and cost—explained for non-experts using papers and official sources.
Stable Diffusion, Midjourney, and DALL-E all rely on the same underlying technology: the diffusion model. Learn how it adds noise to images and then removes it step by step to create pictures, how it differs from GANs and VAEs, and where it's used—explained for non-experts using the DDPM paper and other sources.
What is the 'multimodal AI' behind AI that can now handle more than text—showing it a photo to ask a question, or conversing by voice? The mechanism for handling multiple kinds of data together, how it differs from single-modal AI, and use cases—explained for non-experts, based on official information from Anthropic and Google.
What are 'embeddings,' the foundation beneath RAG and vector search? The mechanism of converting text and images into numeric vectors and expressing closeness of meaning as distance, and the relationship to vector databases and RAG—explained for non-experts, based on official documentation from Microsoft and Anthropic.
What is the Transformer, the foundation shared by ChatGPT's GPT, Google's BERT, and modern generative AI? The self-attention mechanism and parallel processing proposed in the 2017 paper 'Attention Is All You Need,' and why they made the scaling of LLMs possible—explained for non-experts.
What is 'fine-tuning,' the way to raise a pretrained AI for your own use? The mechanism of further training a pretrained model on additional data, how it differs from RAG (baking knowledge in vs. referencing it at run time), and how to choose between them given cost and update frequency—explained from official documentation.
What is prompt injection? A plain-language guide for business leaders to the root cause—LLMs can't tell developer instructions apart from instructions that arrive as data—plus direct vs. indirect injection, how it differs from jailbreaking, real cases like the chatbot talked into selling a car for $1, and OWASP's countermeasures and why a perfect defense is hard.
What is a vector database? A plain-language guide to how it searches data by 'closeness in meaning' rather than keyword matches, covering embeddings, similarity search, and approximate nearest neighbor (ANN) search. We explain its role as the core component behind RAG's retrieval, based on Elastic's official guide.
What is function calling (tool use)? A plain-language guide to how an LLM that only generates text can call external tools like weather APIs or internal systems to take action, based on Anthropic's official documentation. We explain the idea that underpins AI agents and MCP for non-specialists.
What is WebLLM, the engine that runs large language models entirely inside a web browser with no server? A plain-language guide to how it works with WebGPU and WebAssembly, its OpenAI-compatible API, and its privacy and offline benefits, based on the official paper and repository.
What is 'prompt engineering,' the skill of drawing the answers you want out of generative AI? From basics like clear instructions, examples, role-setting, and structuring to chain of thought and prompt chaining—explained for non-experts, based on Anthropic's official guide.
MCP (Model Context Protocol) is the open standard connecting AI applications to external systems. Based on the official documentation, this guide explains why it exists, how the host-client-server architecture works, and what the three primitives — tools, resources, and prompts — actually provide.
What RAG (Retrieval-Augmented Generation) is and how it works, based on AWS's official explainer and the original research paper: how it addresses LLM hallucinations, the four steps using a vector database, and the benefits for business adoption.
A plain-language guide to AI agents based on primary sources: how they differ from generative AI chat and workflow automation, the performance and cost of multi-agent systems, and principles for business adoption.