Glossary

Technical terms explained clearly

A

Agent (AI Agent)

An AI system that can independently plan, make decisions, and take actions to accomplish goals — rather than just answering questions.

AGI (Artificial General Intelligence)

A hypothetical form of AI that could perform any intellectual task a human can — learning, reasoning, and adapting across domains without being retrained for each one.

AI (Artificial Intelligence)

Technology that enables machines to perform tasks that typically require human intelligence — from understanding language to recognizing images to making decisions.

API (Application Programming Interface)

A defined way for software components to talk to each other — usually over the network — using requests, responses, and documented rules.

B

Benchmarks

Standardized tests used to measure and compare AI models — like report cards that show how well a model performs on specific tasks.

C

CI/CD (Continuous Integration/Deployment)

A software development practice where code changes are automatically tested, integrated, and deployed to production.

Cloud Computing

Delivery of computing services — servers, storage, databases, networking, and software — over the internet, typically on a pay-as-you-go model.

D

DevOps

A set of practices and cultural norms that bring development and operations together to deliver software faster, safer, and more reliably.

E

Edge Computing

A distributed computing paradigm that processes data closer to the source, reducing latency and bandwidth usage.

F

Fine-Tuning

The process of taking a pre-trained AI model and training it further on your own data to adapt it to a specific task, style, or domain.

Full-Stack Development

Building software across both the parts users interact with in the browser or app (front end) and the servers, databases, and integrations behind them (back end).

L

LLM (Large Language Model)

A deep learning model trained on vast text data that can understand and generate human-like text.

M

Mechanistic Interpretability

The practice of reverse-engineering AI models to understand how they actually arrive at their answers, rather than treating them as black boxes.

O

Open-Source

Software or AI models whose underlying code or weights are publicly available — anyone can inspect, modify, and use them, often for free.

P

Pipelines

Automated, repeatable sequences of steps that move work forward — from building and testing code to ingesting, transforming, and loading data.

R

RAG (Retrieval-Augmented Generation)

An AI architecture that enhances LLM responses by retrieving relevant context from external knowledge bases before generating answers.

RL (Reinforcement Learning)

A training method where an AI learns by taking actions and receiving feedback — rewards for good choices, penalties for bad ones — until it figures out how to achieve a goal.

S

SEO (Search Engine Optimization)

The practice of improving how visible and compelling your site is in organic search results — through technical health, content relevance, and authority signals.

T

Tokens

The basic units of text that AI models process — roughly word-sized chunks that can be words, parts of words, or punctuation.

U

UI (User Interface)

The visual and interactive layer of a digital product — screens, controls, typography, color, and motion that users see and manipulate.

UX (User Experience)

The overall experience someone has when using a product, service, or system — how easy, efficient, and satisfying it feels from their perspective.

V

Vector Database

A database optimized for storing and querying high-dimensional vector embeddings used in similarity search and AI applications.

W

Weights

The learned numbers inside a neural network that encode its knowledge — the 'settings' the model adjusts during training to get better at its task.