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.
Multi-hop QA
Multi-hop QA answers questions that require reasoning over multiple pieces of evidence, connecting information from different sources.
Knowledge-Grounded QA
Knowledge-grounded QA answers questions using information from an external knowledge source, ensuring responses are factually grounded.
Dialogue System
A dialogue system is an AI system designed to converse with humans in natural language, either for specific tasks or open-ended conversation.
Task-Oriented Dialogue
Task-oriented dialogue systems help users accomplish specific goals like booking appointments, placing orders, or finding information.
Open-Domain Dialogue
Open-domain dialogue systems engage in free-form conversation on any topic without being limited to specific tasks or domains.
Dialogue Act
A dialogue act classifies the communicative function of an utterance in conversation, such as greeting, requesting, informing, or confirming.
Intent Detection
Intent detection is the NLP task of identifying the user's goal or purpose from their natural language input.
Knowledge-Grounded Dialogue
Knowledge-grounded dialogue generates conversational responses informed by specific external knowledge sources, improving accuracy and depth.
Empathetic Dialogue
Empathetic dialogue systems recognize user emotions and respond with appropriate emotional awareness, understanding, and support.
Detokenization
Detokenization is the process of converting a sequence of tokens back into a readable, natural-language string.
Case Folding
Case folding is the text preprocessing step of converting all characters to a uniform case, typically lowercase, to reduce vocabulary variation.
Porter Stemmer
The Porter Stemmer is a widely used algorithmic stemming method that reduces English words to their base stems using a series of suffix-stripping rules.
Unicode Normalization
Unicode normalization converts text to a consistent Unicode representation so that visually identical characters are treated as identical by NLP systems.
Contraction Expansion
Contraction expansion is the text preprocessing step of converting contracted words like "don't" and "I'm" into their full forms "do not" and "I am."
Grammatical Error Correction
Grammatical error correction is the NLP task of automatically detecting and fixing grammatical mistakes in text.
Sentence Boundary Detection
Sentence boundary detection is the NLP task of identifying where one sentence ends and the next begins in a text.
Doc2Vec
Doc2Vec is an unsupervised algorithm that learns fixed-length vector representations for documents of any length.
Subjectivity Detection
Subjectivity detection is the NLP task of classifying text as expressing subjective opinions versus objective facts.
Negation Handling
Negation handling is the NLP challenge of correctly interpreting negation words that reverse or modify the meaning of surrounding text.
Multimodal Sentiment Analysis
Multimodal sentiment analysis combines text, audio, and visual signals to determine sentiment more accurately than text alone.
Dialogue Generation
Dialogue generation is the NLP task of automatically producing conversational responses that are contextually appropriate and natural.
Data-to-Text Generation
Data-to-text generation converts structured data like tables, charts, and databases into natural language descriptions and narratives.
Constrained Generation
Constrained generation produces text that satisfies specific requirements such as including certain words, following a format, or meeting length limits.
Text Rewriting
Text rewriting transforms existing text to change its style, tone, complexity, or structure while preserving the original meaning.
Query-Focused Summarization
Query-focused summarization generates summaries tailored to answer a specific question or address a particular information need.
Dialogue Summarization
Dialogue summarization condenses conversations between two or more participants into concise summaries capturing key points and decisions.
Statistical Machine Translation
Statistical machine translation uses probabilistic models trained on parallel text corpora to automatically translate between languages.
Long-Form Question Answering
Long-form question answering generates detailed, multi-sentence or multi-paragraph answers to complex questions that cannot be answered briefly.
Unanswerable Question Detection
Unanswerable question detection identifies questions that cannot be answered given the available context or knowledge.
Slot Filling in Dialogue
Slot filling in dialogue extracts specific pieces of information from user utterances to complete a structured task or form.
Response Ranking
Response ranking scores and orders candidate responses to select the most appropriate reply for a given conversational context.
Multi-Party Dialogue
Multi-party dialogue involves conversations with three or more participants, requiring tracking of multiple speakers and their interactions.
Syntactic Analysis
Syntactic analysis examines the grammatical structure of sentences to understand how words combine according to the rules of a language.
Anaphora Resolution
Anaphora resolution determines what a pronoun or referential expression refers to in the surrounding text.
Discourse Analysis
Discourse analysis studies the structure and meaning of text beyond individual sentences, examining how sentences connect to form coherent passages.
Information Extraction
Information extraction automatically identifies and extracts structured data from unstructured text documents.
Temporal Expression Extraction
Temporal expression extraction identifies and normalizes references to time, dates, and durations mentioned in text.
Keyword Extraction
Keyword extraction automatically identifies the most important and representative words or phrases in a document.
Topic Modeling
Topic modeling discovers abstract topics that occur across a collection of documents using unsupervised statistical methods.
Text Segmentation
Text segmentation divides a text into meaningful units such as topics, paragraphs, or sections based on content boundaries.
Document Classification
Document classification assigns entire documents to predefined categories based on their overall content and purpose.
Readability Assessment
Readability assessment measures how easy or difficult a text is to read and understand for a target audience.
Language Model
A language model is a probabilistic model that predicts the likelihood of sequences of words, forming the foundation of modern NLP.
BLEU Score
BLEU is an automatic evaluation metric that measures the quality of machine-generated text by comparing it against human reference texts.
ROUGE Score
ROUGE is a set of evaluation metrics that measures text summarization quality by comparing overlap between generated and reference summaries.
Human Evaluation
Human evaluation uses human judges to assess the quality of NLP system outputs, providing the gold standard for measuring text quality.
Optical Character Recognition
Optical character recognition converts images of text, such as scanned documents and photos, into machine-readable text.
Text Mining
Text mining applies NLP and data mining techniques to extract valuable patterns, trends, and insights from large collections of text.
Turn owned content into answers
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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.