AI glossary for content assistants
Plain-English definitions of 13,917 AI terms for branded assistant teams.
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Web Scraping
Web scraping is the automated extraction of structured data from web pages, transforming unstructured HTML content into usable datasets.
Relevance Score
A relevance score is a numerical value assigned to a search result indicating how well it matches a query, used to rank results from most to least relevant.
Search Result
A search result is an individual item returned by a search system in response to a query, typically containing a title, snippet, URL, and relevance metadata.
SERP
SERP (Search Engine Results Page) is the page displayed by a search engine in response to a query, containing organic results, ads, featured snippets, and other elements.
Query Understanding
Query understanding is the process of interpreting a search query to determine user intent, extract entities, and transform the query for better retrieval.
Query Parsing
Query parsing is the process of analyzing and breaking down a search query into structured components like keywords, operators, phrases, and filters.
Query Expansion
Query expansion automatically adds related terms, synonyms, or contextual words to a search query to improve recall and find more relevant results.
Query Suggestion
Query suggestion recommends alternative or refined search queries to users based on popular searches, related topics, and query patterns.
Spell Correction
Search spell correction automatically detects and fixes misspelled query terms to ensure users find relevant results despite typing errors.
Filtered Search
Filtered search narrows search results by applying constraints on specific fields or attributes, such as date ranges, categories, prices, or status values.
Phrase Search
Phrase search finds documents containing an exact sequence of words in the specified order, typically indicated by enclosing the phrase in quotation marks.
Proximity Search
Proximity search finds documents where specified terms appear within a certain distance of each other, balancing between exact phrase matching and independent keyword search.
Wildcard Search
Wildcard search uses special characters like * and ? to match patterns in search terms, enabling searches for words with unknown or variable characters.
Range Search
Range search finds documents with field values falling within a specified numeric, date, or alphanumeric range, enabling queries like price ranges or date intervals.
Geospatial Search
Geospatial search finds documents or records based on geographic location, supporting queries like finding items within a radius or inside a geographic boundary.
Pointwise Ranking
Pointwise ranking is a learning-to-rank approach that independently scores each document for relevance, treating ranking as a regression or classification problem on individual items.
Pairwise Ranking
Pairwise ranking is a learning-to-rank approach that trains models to correctly order pairs of documents, optimizing for relative relevance rather than absolute scores.
Listwise Ranking
Listwise ranking is a learning-to-rank approach that optimizes the entire ranked list at once, directly maximizing ranking metrics like nDCG.
RankNet
RankNet is a pairwise learning-to-rank algorithm that uses a neural network with a probabilistic cross-entropy loss to learn document relevance ordering.
LambdaRank
LambdaRank extends RankNet by weighting pairwise gradients by the change in ranking metrics, directly optimizing for measures like nDCG.
LambdaMART
LambdaMART combines LambdaRank gradients with gradient boosted decision trees, producing one of the most effective learning-to-rank algorithms in practice.
BERT Ranking
BERT ranking uses BERT language models to understand the semantic relationship between queries and documents, dramatically improving search relevance over keyword-based methods.
Query-Document Relevance
Query-document relevance measures the degree to which a document satisfies the information need expressed by a search query, forming the basis of search ranking.
Click-Through Rate in Search
Click-through rate (CTR) in search measures the percentage of users who click on a search result, serving as an implicit indicator of result relevance and quality.
Dwell Time
Dwell time is the duration a user spends on a page after clicking a search result before returning to the search results, indicating content satisfaction.
Search Quality
Search quality encompasses the overall effectiveness of a search system, measured through relevance metrics, user satisfaction, and operational performance indicators.
Apache Lucene
Apache Lucene is an open-source full-text search library written in Java that provides indexing and search capabilities used as the foundation for Elasticsearch and Solr.
Vespa
Vespa is an open-source big data serving engine developed by Yahoo that combines search, recommendation, and machine learning serving in a single platform.
Forward Index
A forward index maps documents to their contained terms and attributes, complementing the inverted index by enabling document-level lookups and attribute access.
Posting List
A posting list is the list of document identifiers (and optionally positions and frequencies) associated with a term in an inverted index.
Term Dictionary
A term dictionary is the vocabulary component of a search index that maps terms to their posting lists, enabling fast lookup of which documents contain each term.
Search Analyzer
A search analyzer is a text processing pipeline that transforms raw text into normalized tokens for indexing and querying, combining character filters, tokenizers, and token filters.
Token Filter
A token filter is a component of a search analyzer that transforms, removes, or adds tokens during text analysis, such as lowercasing, stemming, or adding synonyms.
Character Filter
A character filter preprocesses raw text before tokenization in a search analyzer, handling tasks like stripping HTML, normalizing characters, or mapping special patterns.
Search Stemmer
A search stemmer reduces words to their root or base form during text analysis, enabling matching between different word forms like "running," "runs," and "ran."
N-Gram Tokenizer
An n-gram tokenizer splits text into overlapping sequences of N characters, enabling partial matching, substring search, and handling of languages without word boundaries.
Edge N-Gram
Edge n-gram tokenization generates character sequences starting from the beginning of each token, commonly used to implement autocomplete and prefix-matching search features.
Dense Passage Retrieval
Dense passage retrieval (DPR) uses dual-encoder neural networks to encode queries and passages as dense vectors for efficient semantic similarity search.
Sentence Similarity
Sentence similarity measures how semantically close two sentences are, using vector representations to quantify meaning overlap for search, deduplication, and matching.
Semantic Matching
Semantic matching determines whether two text inputs convey the same meaning or intent, going beyond keyword overlap to understand conceptual equivalence.
Zero-Shot Retrieval
Zero-shot retrieval enables search systems to find relevant documents for queries on topics or domains not seen during training, without requiring domain-specific fine-tuning.
Late Interaction
Late interaction is a retrieval architecture that encodes queries and documents independently but uses token-level interaction for scoring, balancing efficiency with accuracy.
Multi-Vector Search
Multi-vector search represents documents using multiple embedding vectors rather than a single vector, capturing richer semantic information for more accurate retrieval.
Cross-Lingual Search
Cross-lingual search enables finding relevant documents in one language using queries written in a different language, bridging language barriers in information retrieval.
Multilingual Search
Multilingual search enables a single search system to handle queries and documents in multiple languages, providing relevant results regardless of the language used.
Visual Search
Visual search enables finding information using images as queries instead of text, using computer vision and AI to match visual content with relevant results.
Passage Retrieval
Passage retrieval finds and returns specific text passages within documents that are most relevant to a query, rather than returning entire documents.
User-Based Collaborative Filtering
User-based collaborative filtering recommends items by finding users with similar preferences and suggesting items those similar users have liked.
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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?
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Where can I deploy an assistant?
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Can I customize the branding and UI?
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Can I use my own domain?
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Does InsertChat support voice?
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Does InsertChat support vision?
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Zendesk, HubSpot, Shopify, WooCommerce, calendar booking, web search, Perplexity, and webhooks for your own systems.
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Can the agent hand off to a human?
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Do you provide analytics?
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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.