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.
Weighted Source Attribution
Weighted Source Attribution names a weighted approach to source attribution that helps retrieval and search teams move from experimental setup to dependable operational practice.
Weighted Chunk Selection
Weighted Chunk Selection is an weighted operating pattern for teams managing chunk selection across production AI workflows.
Weighted Corpus Filtering
Weighted Corpus Filtering describes how retrieval and search teams structure corpus filtering so the workflow stays repeatable, measurable, and production-ready.
Weighted Query Routing
Weighted Query Routing names a weighted approach to query routing that helps retrieval and search teams move from experimental setup to dependable operational practice.
Weighted Context Budgeting
Weighted Context Budgeting names a weighted approach to context budgeting that helps retrieval and search teams move from experimental setup to dependable operational practice.
Weighted Retrieval Scoring
Weighted Retrieval Scoring is an weighted operating pattern for teams managing retrieval scoring across production AI workflows.
Weighted Passage Matching
Weighted Passage Matching is an weighted operating pattern for teams managing passage matching across production AI workflows.
Weighted Snippet Selection
Weighted Snippet Selection is a production-minded way to organize snippet selection for retrieval and search teams in multi-system reviews.
Weighted Knowledge Refresh
Weighted Knowledge Refresh is an weighted operating pattern for teams managing knowledge refresh across production AI workflows.
Weighted Evidence Tracing
Weighted Evidence Tracing is an weighted operating pattern for teams managing evidence tracing across production AI workflows.
Weighted Query Expansion
Weighted Query Expansion is an weighted operating pattern for teams managing query expansion across production AI workflows.
Weighted Retrieval Auditing
Weighted Retrieval Auditing is an weighted operating pattern for teams managing retrieval auditing across production AI workflows.
Weighted Context Stitching
Weighted Context Stitching names a weighted approach to context stitching that helps retrieval and search teams move from experimental setup to dependable operational practice.
Weighted Search Calibration
Weighted Search Calibration names a weighted approach to search calibration that helps retrieval and search teams move from experimental setup to dependable operational practice.
Weighted Document Hydration
Weighted Document Hydration is a production-minded way to organize document hydration for retrieval and search teams in multi-system reviews.
Weighted Recall Tuning
Weighted Recall Tuning describes how retrieval and search teams structure recall tuning so the workflow stays repeatable, measurable, and production-ready.
Weighted Noise Filtering
Weighted Noise Filtering names a weighted approach to noise filtering that helps retrieval and search teams move from experimental setup to dependable operational practice.
Weighted Intent Routing
Weighted Intent Routing names a weighted approach to intent routing that helps retrieval and search teams move from experimental setup to dependable operational practice.
Weighted Signal Weighting
Weighted Signal Weighting describes how retrieval and search teams structure signal weighting so the workflow stays repeatable, measurable, and production-ready.
Weighted Hybrid Matching
Weighted Hybrid Matching is a production-minded way to organize hybrid matching for retrieval and search teams in multi-system reviews.
Weighted Corpus Segmentation
Weighted Corpus Segmentation names a weighted approach to corpus segmentation that helps retrieval and search teams move from experimental setup to dependable operational practice.
Weighted Evidence Coverage
Weighted Evidence Coverage is an weighted operating pattern for teams managing evidence coverage across production AI workflows.
Zero-Shot Retrieval Pipeline
Zero-Shot Retrieval Pipeline names a zero-shot approach to retrieval pipeline that helps retrieval and search teams move from experimental setup to dependable operational practice.
Zero-Shot Evidence Ranking
Zero-Shot Evidence Ranking is an zero-shot operating pattern for teams managing evidence ranking across production AI workflows.
Zero-Shot Result Fusion
Zero-Shot Result Fusion is a production-minded way to organize result fusion for retrieval and search teams in multi-system reviews.
Zero-Shot Source Attribution
Zero-Shot Source Attribution describes how retrieval and search teams structure source attribution so the workflow stays repeatable, measurable, and production-ready.
Zero-Shot Chunk Selection
Zero-Shot Chunk Selection is a production-minded way to organize chunk selection for retrieval and search teams in multi-system reviews.
Zero-Shot Corpus Filtering
Zero-Shot Corpus Filtering names a zero-shot approach to corpus filtering that helps retrieval and search teams move from experimental setup to dependable operational practice.
Zero-Shot Query Routing
Zero-Shot Query Routing describes how retrieval and search teams structure query routing so the workflow stays repeatable, measurable, and production-ready.
Zero-Shot Context Budgeting
Zero-Shot Context Budgeting describes how retrieval and search teams structure context budgeting so the workflow stays repeatable, measurable, and production-ready.
Zero-Shot Retrieval Scoring
Zero-Shot Retrieval Scoring is a production-minded way to organize retrieval scoring for retrieval and search teams in multi-system reviews.
Zero-Shot Passage Matching
Zero-Shot Passage Matching is a production-minded way to organize passage matching for retrieval and search teams in multi-system reviews.
Zero-Shot Snippet Selection
Zero-Shot Snippet Selection is an zero-shot operating pattern for teams managing snippet selection across production AI workflows.
Zero-Shot Knowledge Refresh
Zero-Shot Knowledge Refresh is a production-minded way to organize knowledge refresh for retrieval and search teams in multi-system reviews.
Zero-Shot Evidence Tracing
Zero-Shot Evidence Tracing is a production-minded way to organize evidence tracing for retrieval and search teams in multi-system reviews.
Zero-Shot Query Expansion
Zero-Shot Query Expansion is a production-minded way to organize query expansion for retrieval and search teams in multi-system reviews.
Zero-Shot Retrieval Auditing
Zero-Shot Retrieval Auditing is a production-minded way to organize retrieval auditing for retrieval and search teams in multi-system reviews.
Zero-Shot Context Stitching
Zero-Shot Context Stitching describes how retrieval and search teams structure context stitching so the workflow stays repeatable, measurable, and production-ready.
Zero-Shot Search Calibration
Zero-Shot Search Calibration describes how retrieval and search teams structure search calibration so the workflow stays repeatable, measurable, and production-ready.
Zero-Shot Document Hydration
Zero-Shot Document Hydration is an zero-shot operating pattern for teams managing document hydration across production AI workflows.
Zero-Shot Recall Tuning
Zero-Shot Recall Tuning names a zero-shot approach to recall tuning that helps retrieval and search teams move from experimental setup to dependable operational practice.
Zero-Shot Noise Filtering
Zero-Shot Noise Filtering describes how retrieval and search teams structure noise filtering so the workflow stays repeatable, measurable, and production-ready.
Zero-Shot Intent Routing
Zero-Shot Intent Routing describes how retrieval and search teams structure intent routing so the workflow stays repeatable, measurable, and production-ready.
Zero-Shot Signal Weighting
Zero-Shot Signal Weighting names a zero-shot approach to signal weighting that helps retrieval and search teams move from experimental setup to dependable operational practice.
Zero-Shot Hybrid Matching
Zero-Shot Hybrid Matching is an zero-shot operating pattern for teams managing hybrid matching across production AI workflows.
Zero-Shot Corpus Segmentation
Zero-Shot Corpus Segmentation describes how retrieval and search teams structure corpus segmentation so the workflow stays repeatable, measurable, and production-ready.
Zero-Shot Evidence Coverage
Zero-Shot Evidence Coverage is a production-minded way to organize evidence coverage for retrieval and search teams in multi-system reviews.
Multi-Hop Retrieval Pipeline
Multi-Hop Retrieval Pipeline names a multi-hop approach to retrieval pipeline that helps retrieval and search teams move from experimental setup to dependable operational practice.
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.