Search-grounded generation
Search-grounded generation is the LLM workflow where every output sentence is anchored to a retrieved source — the production pattern behind Perplexity, ChatGPT Search, Gemini AI Overview, You.com.
Search-grounded generation is the AI-search product pattern: user query → fan-out web search → retrieve N pages → LLM reads them → answer with inline citations to which source supports each claim. Differs from plain [[rag]] in three ways: (1) sources are live web pages, not static index, (2) the generation must explicitly attribute claims, (3) the UX surfaces sources prominently. Production challenges: latency budget (users see streamed answer in ~2s), hallucination prevention (no claims without source backing), source quality filtering (block low-quality sites), citation accuracy (claim → source mapping must hold), recency (favor fresh sources for time-sensitive queries). The dominant LLM-app pattern in 2026 for consumer-facing search products.
When to use search-grounded generation
- AI search apps.
- Any consumer Q&A surface.
Common mistakes
- Generating before retrieval finishes — race condition produces ungrounded text.
- Trusting any cited source — adversarial / SEO-spam sources need filtering.
FAQ
What is search-grounded generation?
Search-grounded generation is the LLM workflow where every output sentence is anchored to a retrieved source — the production pattern behind Perplexity, ChatGPT Search, Gemini AI Overview, You.com.
When should I use search-grounded generation?
AI search apps. Any consumer Q&A surface.
What are the most common mistakes with search-grounded generation?
Generating before retrieval finishes — race condition produces ungrounded text. Trusting any cited source — adversarial / SEO-spam sources need filtering.
Related terms
- Citation extraction — Citation extraction is the technique of attaching source URLs to LLM-generated claims — critical for AI search products to be trustworthy and to give users a way to verify generated content.
- Retrieval-augmented generation (RAG) — Retrieval-augmented generation (RAG) injects relevant documents into the prompt at query time so the model answers from your data instead of its training memory.
- AI search engine — An AI search engine answers a user's query by retrieving relevant web sources and synthesising a cited answer with a language model — the category that includes Perplexity, ChatGPT Search, Claude with web, and Gemini AI Overviews.
Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/search-grounded-generation.md.