AI concepts,
clearly explained.

Models trained to predict the next token of text, which turns out to be a powerful way to read, write, and reason over language.

LLMs read and write in tokens, and they can only consider a limited window of them at once. This shapes cost, latency, and what fits.

Numeric representations of text (or images) that place similar meanings close together, enabling search and retrieval by meaning.

Giving an LLM relevant information at query time by retrieving it from your own data, so answers are grounded and current.

Further training a model on your own examples to change its behavior, style, or format, rather than its knowledge.

Systems where an LLM plans and takes actions through tools in a loop, rather than producing a single response.

How you instruct a model. Clear role, context, output spec, examples, and constraints get dramatically better results.

How you measure whether an AI system is good enough, before and after you ship it.

When a model produces fluent, confident output that is wrong or fabricated. A core risk to design around.

Turning a working prototype into a reliable, observable, and safe system real users depend on.

What it costs and how long it takes to run a model, and the levers that control both.

How organizations move from experiments to durable value with AI, and where it usually goes wrong.

The policies, roles, and controls that keep AI use safe, compliant, and aligned with the organization.

Handling personal and confidential data responsibly when building with AI tools and models.