---
title: "What is AI Search?"
description: "AI search interprets natural-language questions using semantic understanding and large language models, then returns synthesized answers grounded in cited sources. Unlike traditional search, which matches query words to indexed pages, it tracks context and intent for conversational follow-ups. Its pipeline includes query understanding, embeddings, vector search, retrieval, LLM synthesis, and citations; retrieval-augmented generation (RAG) connects generated responses to source material. The article distinguishes consumer tools that search the open web from enterprise systems that retrieve private, governed company data while respecting access permissions. It concludes that AI search can improve direct information access but should not be treated as definitive, and that sensitive business use requires purpose-built platforms with governance, security, and retrieval quality."
---

# What is AI Search?

[Databricks](https://yomu.fyi/company/databricks) · Databricks Staff · Jun 4, 2026

**Type:** Explainer

## Summary

AI search interprets natural-language questions using semantic understanding and large language models, then returns synthesized answers grounded in cited sources. Unlike traditional search, which matches query words to indexed pages, it tracks context and intent for conversational follow-ups. Its pipeline includes query understanding, embeddings, vector search, retrieval, LLM synthesis, and citations; retrieval-augmented generation (RAG) connects generated responses to source material. The article distinguishes consumer tools that search the open web from enterprise systems that retrieve private, governed company data while respecting access permissions. It concludes that AI search can improve direct information access but should not be treated as definitive, and that sensitive business use requires purpose-built platforms with governance, security, and retrieval quality.

## Context

Traditional search matches keywords to indexed pages, while users and organizations increasingly expect natural-language answers. Consumer systems search public information, but enterprise use requires secure access to private documents, knowledge bases, code, tickets, and other governed data.

## Approach / What changed

AI search chains query understanding, embeddings, vector search, retrieval, LLM synthesis, ranking, and citations. Retrieval-augmented generation grounds answers in retrieved source material, while enterprise implementations add access permissions, data governance, and current internal content.

## Takeaways

- AI search combines semantic retrieval with LLM synthesis, producing natural-language answers and citations rather than only ranked links.
- Retrieval-augmented generation retrieves relevant passages before generation, connecting responses to source material instead of relying solely on model training.
- Consumer search tools are not designed for restricted proprietary data; enterprise systems must enforce permissions and governance while grounding answers in trusted internal sources.

**Tags:** [AI](https://yomu.fyi/topic/ai), [Databricks](https://yomu.fyi/topic/databricks), [LLMs](https://yomu.fyi/topic/llm), [Search](https://yomu.fyi/topic/search)

- Source: [Databricks](https://www.databricks.com/blog/ai-search)
- Source URL: https://www.databricks.com/blog/ai-search
- Ingested by Yomu: 2026-08-31T03:31:41.904Z

[Read original post](https://www.databricks.com/blog/ai-search)