Generative AI Hub

Foundational Concepts

Start here if you're new to AI. These articles cover the building blocks that everything else is built on.

What is Generative AI?

AI that creates new text, images, code or audio by learning patterns from data, rather than only classifying what already exists.

Large Language Models (LLMs)

A model trained on vast amounts of text to predict the next token, which is how it produces fluent language.

Neural Networks

Layers of weighted connections that turn an input into a prediction, and the architecture every modern AI model is built on.

Foundation Models

A large model pre-trained once on broad data, then adapted to many downstream tasks instead of being trained per task.

Prompt Engineering & Interaction

Learn how to talk to AI models effectively in different ways, from basic prompting to advanced techniques.

Prompt Engineering

Designing the instructions given to a model so its output is specific, reliable and repeatable.

Zero-Shot, One-Shot, Few-Shot Prompting

Giving a model no examples, one, or a handful, to steer its answer without retraining it.

Context Engineering

Assembling the right information into a model's context window before it answers, rather than relying on the prompt alone.

Conversational AI

Systems that interpret intent across a dialogue and reply in natural language, holding state between turns.

Agentic AI & Automation

See how AI systems are growing from reactive chatbots to autonomous, goal-driven agents.

What are AI Agents?

A program that takes a goal, chooses a tool, acts, reads the result and decides the next step without being told each move.

Agentic AI

AI that plans and acts toward an objective over multiple steps, rather than answering one prompt at a time.

Multi-Agent Systems

Several specialized agents coordinating on one task, where the coordination and handoff design matters as much as the model itself.

AI Orchestration

The layer deciding which model or agent runs next, holding shared state and handling retries when a step fails.

Model Context Protocol (MCP)

An open standard connecting AI models to tools, APIs and databases through one interface instead of custom integrations.

Core Techniques & Infrastructure

Check out which methods and infrastructure make generative AI work in real-time.

Retrieval Augmented Generation (RAG)

Fetching relevant documents at query time and passing them to the model, so answers are grounded in your own data.

Fine-Tuning LLMs

Continuing training on your own examples so a model adopts a task, format or domain it did not learn originally.

Vector Databases

Storage built for embeddings, returning the nearest matches to a query rather than exact keyword hits.

Generative Models & Architectures

Dive deep into the model architectures that do the content generation.

Diffusion Models

A model that generates an image by starting from noise and removing it step by step toward a target.

GANs (Generative Adversarial Networks)

Two networks trained against each other, one generating and one judging, until outputs are hard to tell from real.

Multimodal AI

A model that accepts and produces more than one kind of input, such as text alongside images, audio or video.

Data & Training

Understand how AI models are fed, trained, and improved with data.

Data Augmentation

Expanding a training set by turning existing samples into new variations, so a model generalizes better.

Synthetic Data

Artificially generated data used for training when real data is scarce, sensitive or unbalanced.

AI Hallucinations

Confident model output that is not grounded in its source data, and the failure most likely to reach a user.