AI glossary for content assistants
Plain-English definitions of 13,917 AI terms for branded assistant teams.
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13,917 terms. Open one for definitions and related concepts.
Search Assistant
A search assistant uses language models to understand natural language queries and provide synthesized answers from search results.
LLM Translation
LLM translation uses large language models to translate text between languages, often matching or exceeding dedicated translation systems.
LLM Summarization
LLM summarization uses language models to condense long documents into shorter summaries while preserving key information and meaning.
LLM Classification
LLM classification uses language models to categorize text into predefined classes, often matching purpose-built classifiers with zero-shot ability.
LLM Extraction
LLM extraction uses language models to identify and pull structured data from unstructured text, like names, dates, and entities.
LLM Reasoning
LLM reasoning refers to the ability of language models to perform multi-step logical thinking, deduction, and problem solving.
Math Reasoning
Math reasoning is the ability of language models to solve mathematical problems through step-by-step logical computation and proof.
Code Reasoning
Code reasoning is the ability of language models to understand, analyze, debug, and logically reason about programming code.
Multi-Step Reasoning
Multi-step reasoning is the ability to solve problems that require multiple sequential logical steps, building each step on previous conclusions.
Adapter Layers
Adapter layers are small trainable modules inserted between transformer layers that allow fine-tuning large models by only training the adapters while keeping the base model frozen.
Token Healing
Token healing is a technique that corrects tokenization artifacts at prompt boundaries by reprocessing the last token of a prefix to ensure generation starts from a clean token boundary.
Guided Generation
Guided generation steers an LLM's output by applying constraints, grammars, or scoring functions during the decoding process to ensure outputs conform to desired formats or content.
Logit Bias
Logit bias is a parameter that adjusts the probability of specific tokens being generated by adding a fixed value to their logits before sampling, allowing fine-grained control over LLM output.
Stop Sequences
Stop sequences are strings that, when generated by an LLM, cause the model to stop generating further tokens. They are used to control response boundaries and format outputs precisely.
Multi-Token Prediction
Multi-token prediction trains LLMs to predict multiple future tokens simultaneously rather than just the next token, improving training efficiency and potentially enabling faster inference.
Early Exit
Early exit is a technique that allows LLMs to skip later transformer layers for easier tokens or tasks, reducing computation by halting forward passes before the final layer when confidence is already high.
NLP
NLP stands for Natural Language Processing, the AI discipline that enables machines to read, understand, and generate human language.
NLU
NLU stands for Natural Language Understanding, the AI capability of comprehending meaning, intent, and context from human language input.
NLG
NLG stands for Natural Language Generation, the AI capability of producing fluent human-readable text from data or model representations.
Text Classification
Text classification is the NLP task of assigning predefined categories or labels to text documents based on their content.
Named Entity Recognition
Named Entity Recognition (NER) is the NLP task of identifying and classifying named entities like people, organizations, and locations in text.
Part-of-Speech Tagging
Part-of-speech tagging is the NLP task of labeling each word in a sentence with its grammatical role, such as noun, verb, or adjective.
Dependency Parsing
Dependency parsing is the NLP task of analyzing the grammatical structure of a sentence by identifying relationships between words.
Semantic Parsing
Semantic parsing is the NLP task of converting natural language into a formal, machine-readable representation of its meaning.
Coreference Resolution
Coreference resolution is the NLP task of determining which words or phrases in a text refer to the same real-world entity.
Entity Linking
Entity linking is the NLP task of connecting mentions of entities in text to their corresponding entries in a knowledge base.
Relation Extraction
Relation extraction is the NLP task of identifying and classifying semantic relationships between entities mentioned in text.
Event Extraction
Event extraction is the NLP task of identifying events mentioned in text along with their participants, times, and locations.
Stance Detection
Stance detection is the NLP task of determining the position or attitude of a text's author toward a specific topic, claim, or target.
Hate Speech Detection
Hate speech detection is the NLP task of identifying language that attacks or demeans individuals or groups based on protected characteristics.
Word Tokenization
Word tokenization is the text processing step of splitting text into individual words or word-like units for further NLP analysis.
Sentence Tokenization
Sentence tokenization is the text processing step of splitting text into individual sentences for structured NLP analysis.
Stemming
Stemming is a text processing technique that reduces words to their root form by stripping suffixes, helping group related word variants together.
Lemmatization
Lemmatization is a text processing technique that reduces words to their dictionary base form (lemma) using vocabulary and morphological analysis.
Stopword Removal
Stopword removal is the text processing step of filtering out common words like 'the,' 'is,' and 'at' that carry little meaningful information.
Text Normalization
Text normalization is the process of converting text into a consistent, standard form by handling case, punctuation, whitespace, and other variations.
Spell Checking
Spell checking is the NLP task of detecting and correcting misspelled words in text using dictionaries and statistical models.
Grammar Checking
Grammar checking is the NLP task of detecting and correcting grammatical errors in text, including syntax, agreement, and punctuation issues.
Language Detection
Language detection is the NLP task of automatically identifying which language a piece of text is written in.
Encoding Detection
Encoding detection is the task of determining the character encoding scheme used in a text file or byte stream.
Bag of Words
Bag of Words is a text representation method that models documents as unordered collections of word counts, ignoring grammar and word order.
N-gram
An n-gram is a contiguous sequence of n items (words or characters) from a text, used to capture local patterns and word co-occurrences.
Unigram
A unigram is a single word or token treated as an independent unit in text analysis, equivalent to a 1-gram.
Bigram
A bigram is a pair of consecutive words or tokens from text, used to capture two-word patterns and basic word co-occurrence.
Trigram
A trigram is a sequence of three consecutive words or tokens from text, capturing three-word patterns and local context.
Skip-gram
Skip-gram is a neural network architecture used in Word2Vec that predicts surrounding words given a target word, learning word embeddings.
CBOW
CBOW (Continuous Bag of Words) is a Word2Vec architecture that predicts a target word from its surrounding context words to learn embeddings.
Word Embedding
A word embedding is a dense vector representation of a word that captures its semantic meaning, learned from large text corpora.
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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.