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  5. Retrieval-Augmented Generation (RAG)

Glossary

Retrieval-Augmented Generation (RAG)

التوليد المعزّز بالاسترجاع

A technique that combines a large language model (LLM) with a private knowledge base. When a user asks a question, the system retrieves relevant documents from the knowledge base, then feeds them to the LLM to generate a grounded, accurate answer. Used in AI operator consoles to let staff query internal pricing sheets, SOPs, or client records in natural language—in both Arabic and English.


Related terms

  • Static Site Generation (SSG)A build-time technique that pre-renders all HTML pages before deployment, resulting in ultra-fast load times with no server processing on each request. Used by frameworks like Next.js and Astro. Ideal for marketing sites, landing pages, and bilingual Gulf content hubs where speed and SEO are paramount.
  • AI Proposal GenerationAutomated system that converts incoming lead inquiries into professional, scoped PDF proposals with pricing and timelines. Uses templates and AI to customize each proposal in minutes, increasing close rates and response speed.
  • 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.
  • 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.

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