The vocabulary behind the lessons.
No dumb questions. Use this page during the workshop and keep it afterward.
The small set of AI terms every workshop builds on.
Software that performs tasks associated with human intelligence, such as understanding language, finding patterns, making predictions, or choosing actions.
Why it mattersIt is the broad category. Models, language models, and agents are specific kinds of AI systems.
You’ll hearWe use AI to summarize the feedback.
A trained mathematical system that turns input into a prediction or output.
Why it mattersThe model is the engine that produces an answer. The surrounding product decides what context and tools it receives.
You’ll hearWhich model is this using?
A model trained on large amounts of text to predict and produce language one token at a time.
Why it mattersLLMs power chat assistants and many agents, but they still generate predictions rather than retrieving guaranteed facts.
You’ll hearSend that prompt to the LLM.
A small unit of text that a language model reads or writes. A token may be a word, part of a word, or punctuation.
Why it mattersContext limits, speed, and API prices are measured in tokens.
You’ll hearThat document uses too many tokens.
The instruction or input given to a model.
Why it mattersA clear prompt states the desired outcome, useful context, constraints, and how to recognize a good result.
You’ll hearTry the task again with a clearer prompt.
The finite amount of instructions, conversation, source material, and tool results a model can consider at one time.
Why it mattersImportant information can be omitted or pushed out when the context becomes crowded.
You’ll hearThat conversation filled the context window.
A fluent answer that contains invented or unsupported information.
Why it mattersConfident wording is not evidence. Important claims still need a source or another check.
You’ll hearVerify that citation in case the model hallucinated it.
Giving a model trusted source material and asking it to base its answer on that evidence.
Why it mattersGrounding makes answers easier to verify and reduces unsupported claims.
You’ll hearGround the answer in the workshop materials.
A model operating in a loop with instructions and tools so it can take multiple steps toward a goal.
Why it mattersAn agent can inspect results and continue working instead of stopping after one response.
You’ll hearGive the agent the goal and let it run the checks.
An agent calling an external capability such as reading a file, searching the web, running code, or sending a request.
Why it mattersTools let a model affect systems and gather current evidence, which also means permissions and limits matter.
You’ll hearThe agent needs a tool for that action.