Technical Implementation · Page 12
llms.txt, ai.txt, structured data, and other technical specs for AI search readiness.
148 articles · Page 12 of 13
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.
WebSite Schema and SearchAction Specification for AI Search
Site-level WebSite schema and SearchAction specification: required fields, JSON-LD patterns, and the signals AI search engines use for site identity.
/.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.
.well-known/ai-summary Endpoint Spec for AI Discovery
A specification for a /.well-known/ai-summary endpoint that exposes a site's canonical AI summary, content inventory, and crawl preferences in a single deterministic location.
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 Fine-Tuning for Search?
Fine-tuning for search adapts foundation models—rerankers, embeddings, generators—for retrieval tasks; the canonical reference for AI search engineers.
What Is Knowledge Graph Grounding
Knowledge graph grounding ties LLM answers to structured entities and relations from sources like Google Knowledge Graph and Wikidata so facts can be verified, disambiguated, and cited.
What is query fan-out? Optimizing multi-query retrieval for RAG
Query fan-out in RAG: when to use multi-query retrieval, how to control cost/latency, deduplicate results, and measure impact on grounded answer quality.
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.