AI glossary for content assistants
Plain-English definitions of 13,917 AI terms for branded assistant teams.
Search glossary terms
13,917 glossary pages match your filters.
Category
Browse by letter
Glossary
13,917 terms. Open one for definitions and related concepts.
Open-Source LLM
A language model whose weights and often training code are publicly released, enabling self-hosting, modification, and community development.
Context Stuffing
The practice of filling the context window with as much relevant information as possible to maximize the model ability to generate accurate responses.
Lost in the Middle
A phenomenon where LLMs attend strongly to the beginning and end of long contexts but struggle to use information positioned in the middle.
Cosine Similarity
A mathematical measure of similarity between two vectors based on the angle between them, widely used to compare embeddings in semantic search.
Knowledge Graph
A structured representation of entities and their relationships, used to enhance LLM knowledge retrieval with structured, relational information.
Model Collapse
A degradation phenomenon where models trained on AI-generated data progressively lose diversity and quality across successive generations.
Hallucination Detection
Techniques for automatically identifying when an AI model generates false or unsupported information in its responses.
Few-Shot Learning
The ability of a model to learn and perform a new task from just a handful of examples provided in the prompt context.
MMLU
MMLU (Massive Multitask Language Understanding) is a benchmark that tests language models across 57 academic subjects, from STEM to humanities.
MMLU-Pro
MMLU-Pro is a harder, more rigorous version of MMLU with ten answer choices and improved question quality to better differentiate frontier models.
HellaSwag
HellaSwag is a benchmark that tests common-sense reasoning by asking models to choose the most plausible continuation of a scenario.
ARC Challenge
ARC Challenge is a benchmark of grade-school science questions that require reasoning beyond simple retrieval to answer correctly.
WinoGrande
WinoGrande is a large-scale benchmark testing common-sense reasoning through pronoun resolution in carefully crafted sentence pairs.
TruthfulQA
TruthfulQA is a benchmark that measures whether language models generate truthful answers rather than reproducing common misconceptions.
GSM8K
GSM8K is a benchmark of 8,500 grade-school math word problems that test multi-step arithmetic reasoning in language models.
MATH Benchmark
MATH is a benchmark of 12,500 competition-level mathematics problems testing advanced reasoning across algebra, geometry, and number theory.
HumanEval
HumanEval is a benchmark of 164 hand-written Python programming problems that test code generation ability in language models.
MBPP
MBPP (Mostly Basic Python Programs) is a benchmark of 974 crowd-sourced Python programming tasks testing fundamental code generation.
MT-Bench
MT-Bench is a benchmark that evaluates multi-turn conversation ability using GPT-4 as an automated judge across eight categories.
Chatbot Arena
Chatbot Arena is a crowdsourced platform where users compare anonymous LLM responses side-by-side, producing Elo-based rankings.
Elo Rating
Elo rating is a scoring system adapted from chess that ranks language models based on pairwise comparison outcomes in evaluation arenas.
AlpacaEval
AlpacaEval is an automated evaluation benchmark that uses LLMs to judge model responses against a reference model on 805 instructions.
LMSYS
LMSYS is a research organization that created Chatbot Arena and maintains the most widely referenced open LLM leaderboard.
IFEval
IFEval is a benchmark that measures how well language models follow specific formatting and constraint instructions in their responses.
BBH
BBH (BIG-Bench Hard) is a curated subset of 23 challenging tasks from BIG-Bench where language models previously performed below average humans.
BIG-Bench
BIG-Bench is a collaborative benchmark with over 200 diverse tasks designed to probe the capabilities and limitations of language models.
GPQA
GPQA (Graduate-Level Google-Proof QA) is a benchmark of expert-level questions where even domain specialists with internet access struggle.
SuperGLUE
SuperGLUE is a benchmark suite of eight difficult language understanding tasks that succeeded GLUE as the standard NLU evaluation.
GLUE
GLUE (General Language Understanding Evaluation) is a benchmark suite of nine NLU tasks that became the first standard for evaluating language models.
SQuAD
SQuAD (Stanford Question Answering Dataset) is a reading comprehension benchmark where models extract answers from Wikipedia passages.
Natural Questions
Natural Questions is a QA benchmark using real Google search queries paired with Wikipedia articles, testing realistic information seeking.
TriviaQA
TriviaQA is a QA benchmark of trivia questions with evidence documents, testing both factual knowledge and reading comprehension.
DROP
DROP is a reading comprehension benchmark requiring discrete reasoning operations like counting, sorting, and arithmetic over text passages.
LAMBADA
LAMBADA is a benchmark testing word prediction where the last word of a passage can only be guessed with broad context understanding.
BoolQ
BoolQ is a yes/no question answering benchmark using naturally occurring questions paired with Wikipedia passages.
CommonsenseQA
CommonsenseQA is a benchmark of multiple-choice questions that require everyday common-sense knowledge to answer correctly.
Arena Hard
Arena Hard is an automated benchmark of 500 challenging prompts derived from Chatbot Arena that predicts human preference rankings.
LiveBench
LiveBench is a continuously updated benchmark using fresh questions to prevent contamination, ensuring models cannot memorize answers.
Evaluation Harness
An evaluation harness is a standardized framework for running benchmarks on language models with consistent settings and scoring.
Contamination
Contamination occurs when benchmark test data leaks into model training data, inflating evaluation scores beyond genuine capability.
Leakage
Leakage is the unintended exposure of test or evaluation data to a model during training, compromising the validity of results.
Ceiling Effect
A ceiling effect occurs when a benchmark becomes too easy for top models, losing its ability to differentiate between them.
Saturation
Saturation describes when model performance on a benchmark plateaus near the maximum, reducing the benchmark's evaluative usefulness.
Human Baseline
A human baseline is the performance level achieved by human evaluators on a benchmark, used as a reference point for model comparison.
Inter-Annotator Agreement
Inter-annotator agreement measures how consistently multiple human evaluators rate or label the same AI outputs.
Automatic Evaluation
Automatic evaluation uses algorithms or AI judges to assess language model outputs without requiring human annotators.
Human Evaluation
Human evaluation uses human judges to assess language model outputs for quality, accuracy, helpfulness, and safety.
Preference Evaluation
Preference evaluation compares model outputs by asking judges to select the preferred response from two or more options.
Turn owned content into answers
Use InsertChat to launch a branded assistant visitors can ask directly.
7-day free trial · No card required
Try the FAQ like a visitor.
Open product, pricing, security, integration, and free-tool questions in the same chat your visitors use.
InsertChat
Interactive FAQ
Hey. Pick a question below and see how InsertChat turns FAQs into clear, source-backed answers.
Product FAQ
What is InsertChat?
InsertChat is a white-label AI assistant for your website. Train it, brand it, publish it, and learn from visitor questions.
How does InsertChat use my website content?
Connect approved pages, docs, videos, FAQs, policies, and other sources. InsertChat turns them into source-backed answers and next steps.
Can I control the assistant's tone and sources?
Yes. Choose its sources, tone, welcome message, and prompts so it stays on brand.
How does InsertChat stay accurate?
Answers use approved content and source links. Analytics show unclear or missing answers so you can improve coverage.
Can it collect leads or route support questions?
Yes. InsertChat can collect details, qualify intent, add context, and send chats to the right inbox, CRM, workflow, or person.
Can I control how the assistant behaves?
Yes. Control prompts, model choice, tool access, and the branded assistant experience so behavior stays consistent.
Which AI models can I use?
InsertChat supports multiple model providers. Choose each assistant's model for quality, speed, and cost, or use BYOK.
Can I pick different models for different workflows?
Yes. Use a faster model for common questions and a stronger model for complex reasoning. InsertChat supports that balance per conversation.
Where can I deploy an assistant?
Use a widget, embed, full-page assistant, custom domain, in-app embed, or API. Reuse one setup across surfaces.
Do I need coding skills?
No. Build and deploy AI assistants using our visual builder. The embed code is one line of JavaScript.
Can I customize the branding and UI?
Yes. Customize the assistant name, logo, colors, welcome message, suggested prompts, tone, domain, and white-label presentation.
Can I use my own domain?
Yes. Custom domains are supported, typically via enterprise options.
Does InsertChat support voice?
Yes. Voice dictation and text-to-speech let users speak instead of type.
Does InsertChat support vision?
Yes. Enable vision for assistants when images help clarify a request or context.
What tools and integrations are supported?
Zendesk, HubSpot, Shopify, WooCommerce, calendar booking, web search, Perplexity, and webhooks for your own systems.
Can I control which tools the assistant is allowed to use?
Yes. Tool access is controlled per assistant so you enable only what you need.
Can the agent hand off to a human?
Yes. Configure human handoff so the agent escalates when needed. Full conversation history is passed along.
Do you provide analytics?
Yes. Track chats, leads, feedback, top questions, unanswered questions, most-used sources, and content gaps.
Is it mobile friendly?
Yes. The widget and embeds work well on desktop and mobile with no separate experience needed.
What's the fastest path to a successful deployment?
Start with one assistant and a small set of high-value sources. Iterate using real questions from analytics.
What is the fastest way to get started?
Create an account. Connect one key source. Ask a test question, brand the assistant, then publish it on one page.