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AI2026-05-17

RAG (Retrieval-Augmented Generation)

Also known as: Retrieval-Augmented Generation / 検索拡張生成

A technique that retrieves relevant information from an external knowledge base and grounds an LLM response on it — the mainstream approach for connecting LLMs to up-to-date or proprietary data.

Overview

RAG retrieves relevant material from an external source — a vector DB, search engine, or internal wiki — and injects it into the LLM prompt before generation. Because it grounds the model on fresh or proprietary data without retraining, RAG is the dominant pattern for enterprise LLM deployments.

Common use cases

Internal knowledge Q&A, customer-support bots, product-doc search, legal research, and medical literature search. Google AI Overviews and Claude/ChatGPT web-search features are also RAG patterns. See Building a RAG knowledge base with OpenClaw for a practical implementation guide.

Related Columns

Software Development

Building Internal Knowledge Search with OpenClaw: RAG-Powered AI Agent Guide

Learn how to build a high-accuracy internal knowledge search system using OpenClaw and RAG (Retrieval-Augmented Generation). This guide covers local vector database setup with ChromaDB, Qdrant, and Weaviate, document indexing strategies, and practical deployment for searching across PDFs, Word documents, and internal wikis.

Building Internal Knowledge Search with Qwen3.5-9B & RAG: Enterprise Data AI Guide

A comprehensive guide to building an internal knowledge search system with Qwen3.5-9B and RAG. Covers document ingestion, Japanese-optimized embeddings, vector database selection, chunking strategies, 262K context utilization, citation tracking, and accuracy evaluation methodology.

Amazon S3 Vectors Complete Guide — Reduce AI/RAG Costs by 90% with Native Vector Search Storage [2026]

Complete guide to Amazon S3 Vectors (GA since December 2025). Covers up to 90% cost reduction vs dedicated vector DBs, 2-billion vectors per index, RAG with Bedrock Knowledge Bases, and Python code examples.

Building RAG-Enabled Customer Support AI with Ollama and OpenClaw

This article explains how to build a RAG (Retrieval-Augmented Generation) customer support system by combining Ollama's embedding models with OpenClaw agents. Through vector database integration, you can generate accurate answers from FAQ documents and deploy AI support across multiple channels like LINE and Slack.

Building a RAG-Enabled Internal Knowledge Base AI with Qwen3.5-9B and OpenClaw

Learn how to build a RAG-enabled internal knowledge base AI using Qwen3.5-9B and OpenClaw. This guide covers document ingestion from PDFs, Word files, and internal wikis, vector database integration, and best practices for achieving accurate information retrieval with natural dialogue. We provide AI agent implementation support for companies in Shinagawa-ku, Minato-ku, Shibuya-ku, Setagaya-ku, and Ota-ku to enhance operational efficiency.

Related Terms

LLM (Large Language Model)Vector Database Embedding Grounding Semantic Search

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