What is the primary function of a Large Language Model (LLM)?

Natural language processing. An LLM is a neural network specifically trained on vast amounts of text data to perform natural language processing tasks, such as generating human-like text.

In the context of LLMs, what is a 'token'?

A small chunk of text. A token is a small unit of text that an LLM processes, which can be a whole word, a part of a word, or even a single punctuation mark.

What does the 'context window' of an LLM determine?

The number of tokens processed at once. The context window defines the maximum number of tokens an LLM can consider simultaneously when it is predicting the next token in a sequence.

The physical size of the server

The speed of the internet connection

The number of tokens processed at once

What is the 'knowledge cutoff' of an LLM?

The date training data stopped. The knowledge cutoff is the specific date when an LLM stopped learning from new data, meaning it lacks knowledge of events occurring after that time.

Which term describes the process of generating text by predicting the next token?

Inference. Inference is the operational phase where a generative LLM produces text by calculating the most likely next token based on the input sequence provided.

What is the goal of 'Prompt Engineering'?

Designing optimized input prompts. Prompt engineering is the practice of carefully designing and refining input prompts to guide an LLM toward producing more accurate or desired outputs.

Designing optimized input prompts

What do 'vector embeddings' represent in an LLM?

Numerical semantic meaning. Vector embeddings are compact numerical representations that capture the semantic meaning or context of words and sentences within the model's space.

What is 'few-shot learning' in LLM usage?

Providing examples to improve performance. Few-shot learning involves providing an LLM with a small number of examples of a task to help the model better understand and perform that specific task.

Limiting the model to few answers

Providing examples to improve performance

What does 'Chain-of-Thought' prompting encourage an LLM to do?

Generate intermediate reasoning steps. Chain-of-Thought prompting improves reasoning by instructing the model to generate intermediate logical steps before arriving at the final answer to a problem.

Generate intermediate reasoning steps

What is a 'hallucination' in the context of AI?

Making up facts not grounded in data. Hallucination occurs when an AI generates information that is factually incorrect or not grounded in the provided data, essentially making up details.

Making up facts not grounded in data

How many minutes does "LLM Terms" take to finish?

Expect roughly 5 minutes for a full playthrough. "LLM Terms" has 10 questions on a 20-second-per-question clock, with voice-hosted reveals in between.

Can my players join "LLM Terms" without creating an account?

Players don't need to register. Share the link, they open it, they play. Trivana is built so hosts (classrooms, events, Discord servers) can spin up a game without forcing every participant through a signup flow.

Who hosts "LLM Terms"?

"LLM Terms" is hosted by Jasper, one of Trivana's seven AI gameshow hosts. Jasper narrates every question, reacts to answers in real time, and gives the pack its on-mic personality. You can read Jasper's full profile and hear the voice at /hosts/jasper.

What's the difficulty level of "LLM Terms"?

The pack is rated medium — a mid-level challenge — expect questions that reward genuine familiarity with the topic but don't require deep expertise. Combined with the serious tone, it's a good fit for mixed groups with some knowledge of the topic.

What language is "LLM Terms" in?

Published language: English. Trivana supports ten languages end-to-end (English, Spanish, Hindi, French, German, Portuguese, Japanese, Arabic, Korean, Chinese), and the AI host speaks each of them natively rather than translating text on top of an English recording.

LLM Terms · 10Q AI Trivia

Q: What will I be tested on in "LLM Terms"?

At its core, "LLM Terms" is about Create a 10-question Trivana gameshow about terms for LLM basics, focusing on…. Questions pull from themes including Core LLM definitions, Prompting techniques, Model training stages, Data processing units, AI reasoning methods, and Model limitations and accuracy. Exact questions are held back from the landing page so the first run still feels fresh.

Quick answer

LLM Terms is a 10-question AI-hosted trivia round about Create a 10-question Trivana gameshow about terms for LLM basics, focusing on…, voiced by Jasper and playable in about 5 minutes with no signup.

The round rewards depth of knowledge over quick guesses and the difficulty stays balanced for mixed audiences.

Key facts about LLM Terms

LLM Terms contains 10 multiple-choice questions and takes about 5 minutes to complete.
The round is hosted by an AI voice (Jasper) that reads each question aloud and reacts to your answer in real time.
The pack is delivered in English and is free to play in any modern browser without an account.
Every question was generated by AI and passed Trivana's fact-checking pipeline before publication.
A Large Language Model (LLM) is a neural network trained on vast text data to perform natural language processing tasks, especially language generation[1][2].
Questions cover 6 sub-topics within Create a 10-question Trivana gameshow about terms for LLM basics, focusing on…, including Core LLM definitions, Prompting techniques, Model training stages.

About “LLM Terms”

~5 min
·
10 questions
·
medium
·
serious
·
English

Hosted byVery highJasperEnergetic crowd-pleaserConfident American English with quick comedic timing

This pack is a shareable medium-level check on Create a 10-question Trivana gameshow about terms for LLM basics, focusing on…. Built for educators, L&D teams, and anyone running a review session who doesn't want a dry slide deck or a paper quiz.

Each question runs on a 20-second timer, with answer reveals voiced by Jasper. Jasper plays the room like a late-night host — fast, playful, sharp on reactions. Total run time is about 5 minutes for 10 questions.

The pack covers Core LLM definitions, Prompting techniques, Model training stages, Data processing units, AI reasoning methods, and Model limitations and accuracy. Actual question text is kept off the lander so the first run doubles as a genuine pre-/post-test, not a review of the questions you already saw.

No accounts, no installs — participants open the link on any device and play. The results screen shows each player's score and streak, which is enough for a quick debrief without exporting data. All in English.

What you’ll be tested on

Core LLM definitions
Prompting techniques
Model training stages
Data processing units
AI reasoning methods
Model limitations and accuracy

Questions about this game

FAQ

Expect roughly 5 minutes for a full playthrough. "LLM Terms" has 10 questions on a 20-second-per-question clock, with voice-hosted reveals in between.

What we verified before publishing

Fact-check status: passed

The following claims were verified through Perplexity Sonar before the questions were finalised. The host can reference any of them during play:

A Large Language Model (LLM) is a neural network trained on vast text data to perform natural language processing tasks, especially language generation[1][2].
A token is a small chunk of text, such as a whole word, part of a word, or punctuation, that an LLM processes[3][7].
The context window is the number of tokens an LLM can process or consider at once when predicting the next token[3][4].
The knowledge cutoff is the date when an LLM stopped learning from data and does not have knowledge beyond that date unless updated[3].
Inference is the operation of generating text with a generative LLM by predicting the next token from a sequence of input tokens[1][5].
Prompt Engineering is the process of designing and optimizing input prompts to guide an LLM to achieve desired outputs[1][4].
Vector embeddings are compact numerical representations of words or sentences that capture their semantic meaning or context[1].
Few-shot learning is the ability to provide an LLM examples of a task to enhance its performance on that task[1].
Chain-of-Thought prompting improves an LLM's reasoning by prompting it to generate intermediate steps before the final answer[4].
Pre-training is the first stage of LLM training where the model learns patterns, grammar, and general knowledge from a massive dataset[3].
Parameters are the internal weights and biases that an LLM adjusts to generate responses; more parameters often mean better performance but higher computing costs[3].
Hallucination is the phenomenon where an AI makes up real-world facts or details from a chat history that are not grounded in data[5].

Show all 10 questions, answers, and explanations — full spoilers, only expand after playing

Jasper

Jasper

Quick answer

Key facts about LLM Terms

About “LLM Terms”

What you’ll be tested on

Categories

Questions about this game

What we verified before publishing

Question 1: What is the primary function of a Large Language Model (LLM)?

Question 2: In the context of LLMs, what is a 'token'?

Question 3: What does the 'context window' of an LLM determine?

Question 4: What is the 'knowledge cutoff' of an LLM?

Question 5: Which term describes the process of generating text by predicting the next token?

Question 6: What is the goal of 'Prompt Engineering'?

Question 7: What do 'vector embeddings' represent in an LLM?

Question 8: What is 'few-shot learning' in LLM usage?

Question 9: What does 'Chain-of-Thought' prompting encourage an LLM to do?

Question 10: What is a 'hallucination' in the context of AI?

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