AI glossary for content assistants
Plain-English definitions of 13,917 AI terms for branded assistant teams.
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GloVe
GloVe (Global Vectors for Word Representation) learns word embeddings by factorizing a word co-occurrence matrix from a text corpus.
FastText
FastText is a word embedding model from Meta AI that represents words as bags of character n-grams, handling rare and misspelled words better.
ELMo
ELMo (Embeddings from Language Models) produces contextualized word embeddings using bidirectional LSTMs, where a word's vector changes based on its context.
Sentence Embedding
A sentence embedding is a dense vector representation that captures the semantic meaning of an entire sentence in a fixed-size numerical vector.
Sentence-BERT
Sentence-BERT (SBERT) is a modification of BERT that produces semantically meaningful sentence embeddings for efficient similarity comparison.
SimCSE
SimCSE is a contrastive learning framework for producing high-quality sentence embeddings using simple data augmentation techniques.
Cross-encoder
A cross-encoder is a model that processes two text inputs together to produce a relevance score, providing high accuracy but slower than bi-encoders.
Bi-encoder
A bi-encoder encodes two text inputs independently into vectors, enabling fast similarity search through precomputed embeddings.
Dense Representation
A dense representation encodes text as a compact numerical vector where most values are non-zero, capturing semantic meaning efficiently.
Sparse Representation
A sparse representation encodes text as a high-dimensional vector with mostly zero values, typically based on word frequencies or term weights.
Opinion Mining
Opinion mining is the NLP process of extracting and analyzing subjective opinions, attitudes, and evaluations from text data.
Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis identifies sentiment toward specific aspects or features of a product or service mentioned in text.
Polarity Detection
Polarity detection is the task of classifying text as expressing positive, negative, or neutral sentiment.
Emotion Detection
Emotion detection is the NLP task of identifying specific emotions such as joy, anger, sadness, or fear expressed in text.
Emotion Classification
Emotion classification assigns one or more emotion labels from a predefined taxonomy to a piece of text.
Sentiment Lexicon
A sentiment lexicon is a curated list of words and phrases annotated with their associated sentiment polarity or emotional values.
Conditional Text Generation
Conditional text generation produces text that is guided by specific input conditions such as a prompt, topic, style, or structured data.
Text Completion
Text completion is the task of predicting and generating the continuation of a given text prefix or partial input.
Story Generation
Story generation is the NLP task of creating coherent, creative narrative text with characters, plot, and narrative structure.
Controlled Generation
Controlled generation is the technique of guiding AI text generation to follow specific constraints on style, topic, sentiment, or other attributes.
Paraphrasing
Paraphrasing is the NLP task of rewriting text to convey the same meaning using different words and sentence structures.
Text Simplification
Text simplification is the NLP task of rewriting complex text into simpler language while preserving the core meaning.
Text Style Transfer
Text style transfer is the NLP task of changing the style of text (such as formality, sentiment, or tone) while preserving its content.
Text Infilling
Text infilling is the NLP task of generating missing text that fits naturally within surrounding context on both sides.
Text Summarization
Text summarization is the NLP task of condensing a document into a shorter version that captures the most important information.
Extractive Summarization
Extractive summarization creates summaries by selecting and combining the most important sentences directly from the source document.
Abstractive Summarization
Abstractive summarization generates new sentences to capture the essence of a document, paraphrasing and combining ideas from the source.
Multi-Document Summarization
Multi-document summarization creates a single coherent summary from multiple source documents on the same topic.
Meeting Summarization
Meeting summarization automatically creates concise summaries of meeting transcripts, capturing key decisions, action items, and discussions.
Headline Generation
Headline generation is the NLP task of automatically creating concise, informative titles or headlines for articles and documents.
Key Point Extraction
Key point extraction identifies and extracts the main arguments, findings, or takeaways from a document or discussion.
Machine Translation
Machine translation is the NLP task of automatically translating text from one natural language to another using AI.
Neural Machine Translation
Neural machine translation uses deep learning models to translate text between languages, producing more fluent results than earlier statistical methods.
Zero-shot Translation
Zero-shot translation enables a model to translate between language pairs it was never explicitly trained on by leveraging multilingual representations.
Multilingual Translation
Multilingual translation uses a single model to translate between multiple language pairs, rather than separate models for each pair.
Back Translation
Back translation is a technique of translating text to another language and back to create paraphrases or augment training data.
Parallel Corpus
A parallel corpus is a collection of texts aligned with their translations in another language, used to train machine translation systems.
Post-editing
Post-editing is the process of a human translator reviewing and correcting machine translation output to achieve publication-quality results.
Simultaneous Translation
Simultaneous translation processes and translates speech or text in real-time as it is being spoken or written, with minimal delay.
Low-resource Translation
Low-resource translation addresses the challenge of building translation systems for language pairs with very limited training data available.
Question Answering
Question answering is the NLP task of automatically generating answers to questions posed in natural language.
Open-Domain QA
Open-domain QA answers questions about any topic by retrieving information from a large, general knowledge source like the web or Wikipedia.
Extractive QA
Extractive QA answers questions by identifying and extracting the exact answer span from a given text passage.
Abstractive QA
Abstractive QA generates answers to questions in natural language rather than extracting them directly from source text.
Reading Comprehension
Reading comprehension is the NLP task of answering questions about a given text passage, testing whether the model understands the content.
Table QA
Table QA answers natural language questions by querying and reasoning over structured tabular data.
Visual QA
Visual QA answers natural language questions about the content of images, requiring both vision and language understanding.
Conversational QA
Conversational QA handles question answering within a multi-turn dialogue, tracking context and references across conversation turns.
Turn owned content into answers
Use InsertChat to launch a branded assistant visitors can ask directly.
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InsertChat
Interactive FAQ
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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.