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.
Jina Embeddings
Jina Embeddings is a series of open-source and API-accessible text embedding models from Jina AI known for long-context support (up to 8192 tokens) and strong multilingual retrieval performance.
Typo Tolerance
Typo tolerance allows search engines to return relevant results even when users make spelling mistakes, using edit distance algorithms and fuzzy matching to find near-match terms.
Neural Reranking
Neural reranking uses neural networks to re-score and reorder an initial set of retrieved documents with higher accuracy than first-stage retrieval, using cross-encoder or LLM-based scoring.
SPLADE
SPLADE (SParse Lexical AnD Expansion model) is a learned sparse retrieval model that uses BERT and FLOPS regularization to produce sparse, vocabulary-weighted representations for efficient inverted-index search.
HyDE
Hypothetical Document Embeddings (HyDE) improves zero-shot retrieval by using an LLM to generate a hypothetical answer to the query, then embedding that answer to search the document store.
Maximal Marginal Relevance
Maximal Marginal Relevance (MMR) is a document selection algorithm that balances relevance to the query with diversity among selected documents, preventing redundant context in RAG pipelines.
Contextual Retrieval
Contextual retrieval augments document chunks with document-level context before indexing, using an LLM to prepend explanatory context that makes individual chunks more semantically meaningful in isolation.
Semantic Chunking
Semantic chunking splits documents at natural semantic boundaries — where topic or meaning shifts — rather than at fixed character counts, producing more coherent chunks for embedding and retrieval.
Sparse Retrieval
Sparse retrieval represents documents and queries as sparse vectors with mostly zero values, enabling fast lookup using inverted indexes. BM25 and SPLADE are the main sparse retrieval approaches.
Multi-Vector Retrieval
Multi-vector retrieval represents documents with multiple embedding vectors — one per token, sentence, or section — enabling finer-grained matching than single-vector approaches.
Late Chunking
Late chunking embeds the full document first to capture long-range context, then pools token embeddings within each chunk boundary — combining full-document context with chunk-level granularity.
Adaptive Retrieval Pipeline
Adaptive Retrieval Pipeline is a production-minded way to organize retrieval pipeline for retrieval and search teams in multi-system reviews.
Adaptive Evidence Ranking
Adaptive Evidence Ranking names a adaptive approach to evidence ranking that helps retrieval and search teams move from experimental setup to dependable operational practice.
Adaptive Result Fusion
Adaptive Result Fusion describes how retrieval and search teams structure result fusion so the workflow stays repeatable, measurable, and production-ready.
Adaptive Source Attribution
Adaptive Source Attribution is an adaptive operating pattern for teams managing source attribution across production AI workflows.
Adaptive Chunk Selection
Adaptive Chunk Selection describes how retrieval and search teams structure chunk selection so the workflow stays repeatable, measurable, and production-ready.
Adaptive Corpus Filtering
Adaptive Corpus Filtering is a production-minded way to organize corpus filtering for retrieval and search teams in multi-system reviews.
Adaptive Query Routing
Adaptive Query Routing is an adaptive operating pattern for teams managing query routing across production AI workflows.
Adaptive Context Budgeting
Adaptive Context Budgeting is an adaptive operating pattern for teams managing context budgeting across production AI workflows.
Adaptive Retrieval Scoring
Adaptive Retrieval Scoring describes how retrieval and search teams structure retrieval scoring so the workflow stays repeatable, measurable, and production-ready.
Adaptive Passage Matching
Adaptive Passage Matching describes how retrieval and search teams structure passage matching so the workflow stays repeatable, measurable, and production-ready.
Adaptive Snippet Selection
Adaptive Snippet Selection names a adaptive approach to snippet selection that helps retrieval and search teams move from experimental setup to dependable operational practice.
Adaptive Knowledge Refresh
Adaptive Knowledge Refresh describes how retrieval and search teams structure knowledge refresh so the workflow stays repeatable, measurable, and production-ready.
Adaptive Evidence Tracing
Adaptive Evidence Tracing describes how retrieval and search teams structure evidence tracing so the workflow stays repeatable, measurable, and production-ready.
Adaptive Query Expansion
Adaptive Query Expansion describes how retrieval and search teams structure query expansion so the workflow stays repeatable, measurable, and production-ready.
Adaptive Retrieval Auditing
Adaptive Retrieval Auditing describes how retrieval and search teams structure retrieval auditing so the workflow stays repeatable, measurable, and production-ready.
Adaptive Context Stitching
Adaptive Context Stitching is an adaptive operating pattern for teams managing context stitching across production AI workflows.
Adaptive Search Calibration
Adaptive Search Calibration is an adaptive operating pattern for teams managing search calibration across production AI workflows.
Adaptive Document Hydration
Adaptive Document Hydration names a adaptive approach to document hydration that helps retrieval and search teams move from experimental setup to dependable operational practice.
Adaptive Recall Tuning
Adaptive Recall Tuning is a production-minded way to organize recall tuning for retrieval and search teams in multi-system reviews.
Adaptive Noise Filtering
Adaptive Noise Filtering is an adaptive operating pattern for teams managing noise filtering across production AI workflows.
Adaptive Intent Routing
Adaptive Intent Routing is an adaptive operating pattern for teams managing intent routing across production AI workflows.
Adaptive Signal Weighting
Adaptive Signal Weighting is a production-minded way to organize signal weighting for retrieval and search teams in multi-system reviews.
Adaptive Hybrid Matching
Adaptive Hybrid Matching names a adaptive approach to hybrid matching that helps retrieval and search teams move from experimental setup to dependable operational practice.
Adaptive Corpus Segmentation
Adaptive Corpus Segmentation is an adaptive operating pattern for teams managing corpus segmentation across production AI workflows.
Adaptive Evidence Coverage
Adaptive Evidence Coverage describes how retrieval and search teams structure evidence coverage so the workflow stays repeatable, measurable, and production-ready.
Answer-Aware Retrieval Pipeline
Answer-Aware Retrieval Pipeline describes how retrieval and search teams structure retrieval pipeline so the workflow stays repeatable, measurable, and production-ready.
Answer-Aware Evidence Ranking
Answer-Aware Evidence Ranking is a production-minded way to organize evidence ranking for retrieval and search teams in multi-system reviews.
Answer-Aware Result Fusion
Answer-Aware Result Fusion is an answer-aware operating pattern for teams managing result fusion across production AI workflows.
Answer-Aware Source Attribution
Answer-Aware Source Attribution names a answer-aware approach to source attribution that helps retrieval and search teams move from experimental setup to dependable operational practice.
Answer-Aware Chunk Selection
Answer-Aware Chunk Selection is an answer-aware operating pattern for teams managing chunk selection across production AI workflows.
Answer-Aware Corpus Filtering
Answer-Aware Corpus Filtering describes how retrieval and search teams structure corpus filtering so the workflow stays repeatable, measurable, and production-ready.
Answer-Aware Query Routing
Answer-Aware Query Routing names a answer-aware approach to query routing that helps retrieval and search teams move from experimental setup to dependable operational practice.
Answer-Aware Context Budgeting
Answer-Aware Context Budgeting names a answer-aware approach to context budgeting that helps retrieval and search teams move from experimental setup to dependable operational practice.
Answer-Aware Retrieval Scoring
Answer-Aware Retrieval Scoring is an answer-aware operating pattern for teams managing retrieval scoring across production AI workflows.
Answer-Aware Passage Matching
Answer-Aware Passage Matching is an answer-aware operating pattern for teams managing passage matching across production AI workflows.
Answer-Aware Snippet Selection
Answer-Aware Snippet Selection is a production-minded way to organize snippet selection for retrieval and search teams in multi-system reviews.
Answer-Aware Knowledge Refresh
Answer-Aware Knowledge Refresh is an answer-aware operating pattern for teams managing knowledge refresh across production AI workflows.
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.