AI 101 - AI Midday

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AI 101 — What are scaling laws?

Scaling laws are the empirical rules describing how an AI model's performance improves in a smooth, predictable curve as three things grow: the size of the model, the amount of data it trains on, and the computing power spent training it. They are not physics — nobody proved them in a lab the way gravity was proven — they are patterns that kept holding every time someone measured them, which is why the entire AI industry now plans around them. The pattern got its canonical statement in a Janua

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AI 101 — What is prompt engineering?

Prompt engineering is the practice of writing instructions for an AI model so it gives you what you actually wanted — from a one-line question in a chat box to the multi-paragraph specifications companies embed in their products. "Prompt" is just the text you send the model; "engineering" is the unglamorous work of making that text specific enough to work. Why it matters right now. Prompting is still how almost everyone touches AI, and the craft keeps getting documented rather than disappearin

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AI 101 — What is vibe coding?

Vibe coding is writing software by describing what you want in plain language and letting an AI write the code — while you judge the result by whether it works, not by reading every line. Andrej Karpathy, co-founder of OpenAI, named it in a February 2025 post; by November, Collins Dictionary had named "vibe coding" its word of the year for 2025. Why it matters right now. Code is the one thing AI does that people actually depend on, and the numbers show it: Stack Overflow's 2025 Developer Surve

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AI 101 — What is AI inference?

Every answer an AI gives you is inference: running a trained model on new input to produce an output. Training is how a model learns; inference is how it works. If training is teaching, inference is doing. Why it matters right now. Training makes the headlines — a new model, a bigger cluster, a record run — but training happens once per model, while inference happens every time anyone asks a question. That asymmetry is why the money has shifted: Google Cloud describes inference as the phase "wh

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AI 101 — What is AGI?

AGI — short for artificial general intelligence — is a hypothetical AI system that could do any intellectual job a person can do, instead of being good at one narrow task. Every AI you can actually use today is narrow: it writes, translates, spots patterns in scans, plays games — each system built for its lane. AGI names the destination where one system covers all the lanes. It is a goal nobody has reached, not a product anyone can buy. Why it matters right now "AGI" is one of the most-used w

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AI 101 — What is a large language model?

A large language model (LLM) is a very big neural network trained on enormous amounts of text to do one job well: predict the next piece of a text, given everything that came before it — then tuned so that when you talk to it, it actually helps instead of rambling. ChatGPT, Claude, Gemini, Llama, DeepSeek: every chatbot in the news is an LLM, and most AI features in the software you already use have one quietly bolted inside. That prediction job sounds too simple to matter, and it is the whole

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AI 101 — What is a neural network?

A neural network is a computing system made of layers of simple math functions whose internal numbers are tuned by examples until the whole thing gets the right answers — a design loosely inspired by the brain, and the foundation of every AI system in the news. ChatGPT, Midjourney, AlphaFold: all neural networks, all variations on the same idea. The concept is older than computers. In 1943, Warren McCulloch and Walter Pitts described an artificial neuron — a thing that takes inputs and fires w

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AI 101 — What is CUDA?

CUDA is the software layer that lets ordinary programs run on NVIDIA's graphics chips — and it is the foundation nearly all modern AI is built on. Short for Compute Unified Device Architecture, it first shipped on February 16, 2007, and it turned graphics cards — machines designed to draw video games — into general-purpose computers that any developer could program. Before CUDA, using a graphics chip for serious math meant writing code in the chip's own graphics language, in fragments, with lit

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AI 101 — What is overfitting?

Overfitting is when a model learns its training data too well — it gets the examples it has already seen almost perfect, then performs worse on anything new. Since the entire job of a model is to handle cases nobody showed it, overfitting is the most common way an AI system looks brilliant in a demo and disappoints in production. The term shows up in almost every model release, fine-tuning guide and benchmark write-up, and it is rarely explained. The short version: a model is only worth anythin

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AI 101 — What is reinforcement learning?

Reinforcement learning is how a machine learns by trial and error: it tries things, notices which attempts earned a reward, and drifts toward the behaviour that earns more of it. There is no answer key involved. In the supervised learning most people picture, someone hands the model millions of labelled examples and it learns to copy them. In reinforcement learning, nobody says what the right move is — the machine acts, gets a number back telling it how well that went, and adjusts. The classic

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AI 101 — What is LLM-as-a-judge?

If you read a week of AI news, you'll notice almost nobody grades models by hand any more. When a lab says its new model is better at writing, summarising or handling customers, the number usually came from another AI. An LLM judge is a language model used to grade the work of another model — or of your own AI feature — when no piece of code can decide whether the answer was good. The industry term is LLM-as-a-judge, and the job is simple to state: read a question and an answer, then return a s

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AI 101 — What is prompt caching?

Prompt caching is a way to make an AI model skip re-reading text it has already read. The provider saves its internal working state for the opening section of your prompt, and the next request that starts with the exact same text picks up from there — cheaper and faster. It sounds like a developer-housekeeping detail. It is now one of the largest line items in how AI gets priced, and one of the most common ways a working product quietly runs up a bill. Why it matters right now On September 2

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