125 articles · Page 10 of 11
Video Sitemap Specification for AI Search Citations
Video sitemap specification for AI search citations: required tags, content_loc and player_loc, thumbnails, duration, and transcript pairing patterns.
Viewport Meta and AI Mobile Rendering
How the viewport meta tag affects mobile-first AI rendering, why misconfigurations cause silent citation losses, and the safe defaults to ship.
Web Vitals and Core Performance Metrics for AI Citation Eligibility
Web Vitals and core performance metrics for AI citation eligibility: LCP, INP, CLS thresholds plus TTFB and HTML-size budgets AI crawlers respect.
WebP vs AVIF for AI image citations
WebP vs AVIF for AI image citations: format support, compression benchmarks, and fallback patterns to ensure thumbnails render in answer cards.
/.well-known/ai-plugin.json Manifest Specification
/.well-known/ai-plugin.json manifest spec—field-by-field reference, auth options, OpenAPI integration, and ChatGPT plugin sunset migration to Custom GPTs and MCP.
What is Chunking for RAG
Chunking for RAG explained: how splitting documents into retrievable units shapes citation accuracy across fixed-size, recursive, semantic, and sentence-window strategies.
What is Context Window Engineering
Context window engineering is the discipline of curating, ordering, and budgeting tokens in an LLM's context to maximize accuracy and minimize hallucinations.
What is Query Fan-Out in AI Search
Query fan-out is how AI search engines decompose a single question into many parallel sub-queries to retrieve diverse sources and synthesize a grounded answer.
What Is RAG (Retrieval-Augmented Generation)
RAG (retrieval-augmented generation) pairs a retriever and an LLM so answers are grounded in fresh, citable sources rather than the model's parametric memory alone.
What is Reranking for AI Search
Reranking refines retrieval results before grounding by scoring query-document pairs with a cross-encoder, sharply improving citation accuracy in RAG.
What Is Semantic Search?
Semantic search uses meaning, not keywords, to retrieve results. Learn how vector embeddings, dense retrieval, and AI models power modern search.
What Is a Vector Embedding for Search
A vector embedding is a fixed-length list of numbers that captures the meaning of text so similar concepts sit close together, powering semantic search and RAG.