Tech & AI Terms, Explained
AI vocabulary moved from research papers into the evening news faster than anyone could explain it, and the rest of technology was already jargon. This section covers both: how AI models actually work, from training and inference to context windows and agents, alongside the software and security terms that show up in the same articles, including zero-days, technical debt and APIs. The largest section here, and the fastest growing.
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What are AI Agents?
AI that acts instead of just answering: the plan-do-check loop, and why the loop is also the risk.
What is an AI Hallucination?
Why AI states made-up facts with total confidence, and why you can't hear it happening.
What is a Neural Network?
Math that learns the rules from examples instead of having them written, and why that wins.
All 59 terms
What is an Abstraction Layer?
Simple on top, complexity hidden underneath — and swappable without breaking anything.
What are AI Agents?
AI that acts instead of just answering: the plan-do-check loop, and why the loop is also the risk.
What are AI Benchmarks?
The scores behind every AI launch, what they measure, and why the leader can still disappoint.
What is an AI Hallucination?
Why AI states made-up facts with total confidence, and why you can't hear it happening.
What is an API?
The fixed agreement that lets two programs work together without knowing each other's insides.
What is Backpropagation?
How one final error becomes a precise adjustment for millions of settings — Goal Seek at scale.
What is the Bias-Variance Tradeoff?
Too rigid vs. too impressionable — and why total error is lowest in between.
What is Chain-of-Thought Reasoning?
Why a model that shows its work answers better, and what the thinking costs.
What is a Context Window?
How much text an AI can hold in mind at once — and why long chats forget their start.
What is Edge Computing?
Doing the work on the device instead of a data centre, and the three situations that require it.
What is End-to-End Encryption?
Why nobody in the middle can read the message, and what it still leaves visible.
What is Fine-Tuning?
Training a finished model a little more on your own data, and how that differs from RAG.
What does an Idempotent API mean?
Why ten clicks charge you once — the property that makes retries safe.
What is Inference (in AI)?
The model doing its actual job, and why the cheap half ends up costing the most.
Machine learning vs AI: what's the difference?
AI is the goal. Machine learning is the method that took over. The words are not interchangeable.
What is a Mixture of Experts (MoE)?
Huge model, a small slice awake per question, and why that keeps frontier models affordable.
What is Model Distillation?
Training one model on another model's answers: to shrink your own, or to catch up to a rival's.
What is a Multimodal Model?
AI that takes images, audio and text together, and what that costs in speed and reliability.
What is a Neural Network?
Math that learns the rules from examples instead of having them written, and why that wins.
Open-Source vs Closed-Source AI Models: What's the Difference?
Who controls the model, who sees your data, and who gets paid per request.
Overfitting vs. Underfitting: What's the Difference?
Memorized the answers vs. never opened the book — and the score gap that exposes both.
What is Prompt Injection?
Hidden orders inside content an AI reads — and why the attacker's text wins.
What is Quantization?
Storing a model's numbers less precisely — half the memory, barely any loss.
What is Retrieval-Augmented Generation (RAG)?
Make the AI answer from your documents — retrieval first, generation second, receipts included.
What is RLHF (Reinforcement Learning from Human Feedback)?
The step that turns a text predictor into an assistant, and what it quietly teaches the model.
What is Stochastic Gradient Descent (SGD)?
Millions of dials, one wrongness score — how models actually learn, tiny step by tiny step.
What is Synthetic Data?
Training data made by machines, why labs increasingly rely on it, and how it goes wrong.
What is Technical Debt?
Shortcuts and aging systems that bill the future — principal, interest, and repayment.
What is Temperature in AI?
The dial between same-answer-every-time and creative gambles — and where products set it.
What is Tokenization?
How AI chops text into chunks before reading — and why some languages cost more.
What is a Transformer (in AI)?
The design behind the T in GPT: every word attends to every other word, all at once.
What are Vector Embeddings?
Meaning turned into coordinates — how machines find what's similar without matching a single word.
What are breadcrumbs on a website?
The small line of links at the top of a page showing the path from the home page down to where you are.
What is a GPU (graphics processing unit)?
The chip that does thousands of calculations at once. Built for graphics, now the engine of AI.
What is a knowledge cutoff?
The date a model stopped learning. It limits what the model knows, not what it can be told.
What is a large language model (LLM)?
A program that predicts what text comes next, then taught separately to answer rather than ramble.
What is a system prompt?
The hidden instructions a chatbot reads before your conversation starts. Every reply is shaped by them.
What is a VPN (virtual private network)?
A service that encrypts your internet traffic and sends it out through its own server, so others see less of you.
What is AGI (artificial general intelligence)?
The hypothetical AI that could handle anything a person can. It does not exist yet, and there is no agreed test for it.
What is an agent harness?
The program wrapped around an AI model that actually runs the tools and repeats the loop.
What is cloud computing?
Renting computers over the internet instead of owning them. Most of the apps you use live there.
What is context engineering?
Deciding everything a model sees before it answers, rather than how the question is worded.
What is recursive self-improvement?
An AI helps build the next AI, which builds the one after that, better each round.
What is Retrieve-for-Train?
Search for camping gear and get a tent, a bag, a stove and a lamp, not ten tents. How Google moved the thinking out of the search bar.
What is training (in AI)?
The process that builds a model by example. Chatting with it afterwards changes nothing.
What is a Zero-Day Vulnerability?
A hole only the attacker knows about, why it costs a fortune, and what actually breaks most systems.
What is a Zero-Knowledge Proof (ZKP)?
Prove something is true without revealing why — the cryptography behind private verification.
What is an API key?
The string that tells a service which account is calling, and which one to bill. Not a password.
What is a cache?
Copies kept close so the slow trip isn't made twice. Why it makes things fast, and why clearing it fixes things.
What is latency?
The wait between asking and the answer starting to arrive. Not the same as slow internet, and more capacity won't fix it.
What are scaling laws?
The pattern behind the AI race: bigger models get better on a curve you can forecast, and each step costs ten times the last.
What is AI alignment?
Getting AI to pursue what we meant, not the letter of what we said. Why that gap exists and why it widens as systems get capable.
What is High-Bandwidth Memory (HBM)?
A stack of memory chips on the processor's own package, joined by a path over a thousand wires wide. It exists because arithmetic outran delivery.
What is a Tensor Processing Unit (TPU)?
Google's chip that keeps only one kind of arithmetic, the grid multiplication neural networks live on, and spends everything saved on capacity.
What is CUDA?
Nvidia's software for its graphics chips: write ordinary code, run it as thousands of tasks at once. The catch: it runs on Nvidia's chips alone.
What is a Compute Cluster?
Hundreds or thousands of separate computers wired together to run one job. The hard part is not assembling them, it is keeping them in agreement.
Single-core vs multi-core: what's the difference?
One processing unit per chip, or several independent ones sharing it. Why chips stopped getting faster, and what the extra cores actually do for you.
ASIC vs FPGA: what's the difference?
One chip is manufactured permanently for a single job; the other is rewired by loading a file. The choice is a bet on volume and certainty.
Slurm vs Kubernetes: what's the difference?
Two programs that decide which work runs on which machines. One runs a queue for jobs that finish; the other keeps services alive forever.
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