microCMS × RAG / Retrieval-Augmented Generation
microCMS × RAG (Retrieval-Augmented Generation)
Want to implement AI chatbots or internal AI search, but worried about hallucinations (generating information that differs from facts)? Can't keep up with the latest information? The combination of microCMS and RAG (Retrieval-Augmented Generation) fundamentally solves these challenges.
We build RAG systems that use content managed in microCMS as the knowledge base, with LLMs generating responses by referencing only that content. Every time content is updated, a webhook fires and automatically syncs the vector DB, realizing an AI system that always answers with the latest official information.
Furthermore, by also using the same microCMS content for LLMO (LLM Optimization), we realize a unique architecture that integrates both "internal RAG" and "external LLMO" management in one CMS.
RAG Market Reality
~78%
Enterprise RAG adoption intent (2026)
Rapidly expanding as primary generative AI utilization method
~40%
Hallucination reduction rate after RAG adoption
Accuracy improvement from official knowledge reference
~60%
Customer support cost reduction rate
Average among companies that adopted RAG chatbots
What is RAG (Retrieval-Augmented Generation)?
RAG (Retrieval-Augmented Generation) is a technology where, when an LLM (Large Language Model) generates responses, it retrieves relevant information from an external knowledge base in real-time and incorporates that information as context to generate answers.
Without RAG, LLMs answer only from knowledge at training time, creating challenges with up-to-date information and hallucinations (generating information that differs from facts). By implementing RAG, you can provide accurate, reliable answers that always reference your latest content.
Combining microCMS with RAG realizes the ideal operational cycle: 'when content is updated, AI answers are updated immediately.'
microCMS × RAG Architecture
STEP 01
Content Management
Manage FAQ, manuals, and product information in microCMS. Design schemas in RAG-optimized format.
STEP 02
Webhook Trigger
Webhook automatically fires on content updates, launching the sync pipeline.
STEP 03
Vectorization & DB Sync
Content is chunk-split, embeddings generated, and automatically synced to the vector DB.
STEP 04
Semantic Search
User questions are vectorized and highly relevant content is retrieved from the vector DB.
STEP 05
RAG Response Generation
Search results are passed to the LLM as context to generate accurate answers based on official information.
💡 By leveraging microCMS webhook functionality, the vector DB is automatically synced every time content is updated. No manual data update work is needed — the latest content is always reflected in the RAG system.
Why microCMS Excels at RAG
API-First Makes Vector DB Integration Easy
Since microCMS delivers all content via REST API, ingestion into vector DBs like Pinecone, pgvector, and Weaviate is extremely simple. Content can be vectorized directly from API responses without custom scripts.
Webhooks Keep Everything Up-to-Date
Every time content is updated, added, or deleted, a webhook fires and automatically syncs the vector DB. This fundamentally solves the problem of 'AI answering with outdated information,' enabling real-time accurate responses.
Permission Management Reflected in RAG
By reflecting microCMS content management permissions in the RAG system, you can implement fine-grained access control: 'confidential information not shown to general employees is also not answered by AI.' This is a critical requirement for internal knowledge RAG.
Content Schema Improves RAG Accuracy
microCMS's rich content schema (title, body, category, tags, related content, etc.) can be leveraged for RAG chunk splitting and metadata filtering, dramatically improving search accuracy and answer quality.
Common Challenges We Solve
- Want to build a customer support bot that references product FAQ and support documents
- Want to build an internal knowledge system where employees can search and ask questions about manuals and regulations using AI
- Want to use microCMS content as a RAG knowledge base
- Struggling with AI chatbot hallucinations
- Want AI answers to automatically update whenever content is updated
- Want to integrate internal RAG and external LLMO management in one CMS
- Want end-to-end support from RAG system construction to operations
3 Core Solutions
Solution 01
microCMS × RAG Chatbot
AI Chatbot That Only References Official Content
We build AI chatbots without hallucinations by using FAQ, product information, and support documents managed in microCMS as the knowledge base. Every time content is updated, a webhook fires and automatically syncs the vector DB — delivering a system that always answers with the latest official information.
- microCMS Webhook → auto vector DB sync pipeline
- RAG response generation using OpenAI / Claude API
- Hallucination-prevention design referencing only official content
- Display of source content links for answers
- Embedding support for websites, Slack, LINE, and more
Solution 02
Internal Knowledge RAG Platform
microCMS as Single Source of Truth
We centralize internal regulations, manuals, meeting minutes, and technical documents in microCMS, then build an internal AI system where employees can search and ask questions in natural language via RAG. We fundamentally solve the problem of scattered internal information — 'I don't know where to find what' — while keeping information fresh.
- Internal document migration to microCMS and schema design
- Department/permission-based content access control (reflected in RAG)
- Natural language internal information search and Q&A
- Integration with update history and version management
- Integration with Slack / Teams / internal portals
Solution 03
RAG × LLMO Integrated Architecture
Manage Internal RAG and External LLMO from One CMS
The most differentiated unique position is an architecture that uses microCMS as the foundation for both 'internal RAG' and 'external LLMO.' By using the same content for both internal AI system response generation (RAG) and citation optimization for ChatGPT/Perplexity (LLMO), you simultaneously achieve centralized content management and maximized AI utilization.
- Integrated content architecture design centered on microCMS
- Vector DB construction for internal RAG (Pinecone / pgvector)
- Auto JSON-LD generation and structured data optimization for external LLMO
- Content update → RAG sync + LLMO optimization automated pipeline
- Effectiveness measurement dashboard for internal AI use and external AI citations
RAG × LLMO Integrated Position
By placing microCMS at the center, you can integrate both "internal RAG" and "external LLMO" AI utilization under one content foundation. This is Preferred Inc.'s unique architecture that maximizes both internal AI and external AI citations without duplicating content management.
microCMS
Single Source of Truth
Internal RAG
Knowledge Search / FAQ Bot / Internal AI
External LLMO
ChatGPT Citation / AI Search Optimization
Maximized AI Utilization
Unified management of internal & external AI
Tech Stack
CMS
- microCMS
Vector DB
- Pinecone
- pgvector
- Weaviate
- Qdrant
AI / LLM
- OpenAI API
- Claude API
- Gemini API
RAG Framework
- LangChain
- LlamaIndex
- Vercel AI SDK
Infra & Automation
- Next.js
- Vercel
- GitHub Actions
- Cloudflare Workers
Related Services
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