Glossary

Performance Optimization

تحسين الأداء

Optimizing site speed through image compression, caching, CDN delivery, and code splitting. Target: Core Web Vitals (LCP < 2.5s, FID < 100ms, CLS < 0.1). Faster sites rank higher and convert better.

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Web Apps & Next.js

Related terms

Core Web VitalsGoogle's three quantified user-experience signals that are ranking factors: Largest Contentful Paint (LCP, target < 2.5 s), Interaction to Next Paint (INP, target < 200 ms), and Cumulative Layout Shift (CLS, target < 0.1). Gulf sites with heavy Arabic fonts and right-to-left layouts must be explicitly tuned — naive ports from LTR templates commonly fail CLS and LCP thresholds.
Answer Engine Optimization (AEO)Structuring content so that AI-powered answer engines — Google AI Overviews, ChatGPT Search, Perplexity, and Bing Copilot — pull your page as the cited source. Requires concise, direct answers in the opening paragraph, FAQ schema, and authoritative backlinks. Distinct from GEO: AEO focuses on being the cited source, GEO focuses on content completeness.
Largest Contentful Paint (LCP)The time from page navigation start until the largest visible image or text block is rendered. Google's threshold for a 'good' score is under 2.5 seconds. Common culprits on Gulf bilingual sites: unoptimised hero images, render-blocking Arabic web fonts loaded from external CDNs, and server response latency when hosting is geographically distant from Saudi Arabia or UAE data centres.
Generative Engine Optimization StrategyA documented editorial and technical plan for making a brand consistently visible inside AI-generated answers across ChatGPT, Claude, Gemini, and Perplexity. Key pillars: publishing factual, citable long-form content; earning mentions on authoritative third-party sites; structuring data with schema markup; maintaining a verified Google Business Profile; and monitoring AI-citation frequency with tools like BrandMentions or Profound. In Gulf markets, GEO strategy must account for Arabic language queries separately from English — AI models have distinct training data distributions per language.

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