If your marketing and docs content lives in a headless CMS, you already have the best raw material for AI: product pages, FAQs, changelogs, tutorials, feature docs. RAG (Retrieval-Augmented Generation) lets you use that content as a controlled knowledge base so AI outputs are grounded in your actual copy—rather than guessing.
This guide is a practical blueprint for building RAG on top of a headless CMS like ElmapiCMS.
Table of Contents
- What RAG is (in one paragraph)
- What content to include (and exclude)
- Modeling content for RAG in a CMS
- Chunking strategy (how to split content)
- Embedding + indexing
- Retrieval + prompting (answers with citations)
- Keeping it fresh: webhook-driven updates
- Pitfalls to avoid
What RAG is (in one paragraph)
RAG works like this:
- You store your content as documents (from your CMS)
- You create embeddings and index them in a vector database
- When a user asks a question, you retrieve the most relevant chunks
- You give those chunks to the model and ask it to answer using only those sources
The result: fewer hallucinations, more accuracy, and answers that match your product messaging.
What content to include (and exclude)
Include:
- FAQs
- product docs and feature pages
- pricing explanations
- onboarding/tutorials
- changelog/release notes
- integration guides
Exclude or carefully gate:
- drafts
- private/internal notes
- PII and customer data
- legal content that must be exact unless reviewed
If you run multiple sites/projects in the same CMS, treat each as a separate knowledge domain (or at least tag them).
Modeling content for RAG in a CMS
RAG gets dramatically better when your content is well-structured.
Recommended fields per “knowledge document”:
titleslugtype(faq, docs, tutorial, marketing, changelog)body(rich text/MDX/plain)summary(optional)tags(product area, feature, audience)updatedAtcanonicalUrl(or generate from routing)
Also store these as metadata on each chunk:
- source URL
- document type
- locale
- last updated timestamp
That metadata becomes your filtering and citation system.
Chunking strategy (how to split content)
Bad chunking kills RAG. Practical rules:
- Split by headings first (H2/H3)
- Keep chunks around 200–800 tokens worth of text
- Preserve the title and heading hierarchy as context
- For FAQ: one Q/A per chunk
- For changelog: one release entry per chunk
Include “breadcrumb context” in each chunk:
- document title
- section heading
- optional: product area
This improves retrieval and helps the model cite correctly.
Embedding + indexing
Pipeline:
- Export published entries from the CMS (API or webhook)
- Normalize to plain text (strip markup, keep headings)
- Chunk + attach metadata
- Create embeddings
- Upsert into vector store
Vector store options (common):
- Postgres + pgvector
- managed vector DBs
- search engines with vector support
You also usually want a keyword index (BM25) alongside vectors for hybrid retrieval.
Retrieval + prompting (answers with citations)
The best “marketing RAG” behavior includes citations and safe refusal.
Practical prompt requirements:
- Answer using ONLY the provided sources
- If sources don’t contain the answer: say so and suggest where to look
- Provide citations (URLs) per claim or at least per paragraph
UI tip: show:
- the answer
- a “Sources” list with clickable links (from CMS slugs)
This builds trust and drives users back into your site content.
Keeping it fresh: webhook-driven updates
Treat your CMS as the source of truth:
- On publish/update: re-chunk, re-embed, upsert vectors
- On unpublish/delete: remove vectors for that document
Use a webhook to trigger updates. Store a content “version” or updatedAt and make the pipeline idempotent.
Pitfalls to avoid
- No citations: users can’t verify; conversions drop.
- Indexing drafts: the AI leaks unreleased features.
- Huge chunks: retrieval becomes fuzzy; answers get generic.
- No filters: your app answers “pricing” using an unrelated blog post.
- PII risk: never include customer data in the knowledge base.
Related posts:
- How to Model Relations in a Headless CMS (Beginner Guide) – Structure linked content (authors, categories, FAQs) so it's easy to chunk and filter for RAG.
- Headless CMS Content Modeling: How to Build Reusable Page Sections (Blocks) – Well-structured content types and metadata improve RAG retrieval.
- How to Build a Simple Blog Using ElmapiCMS and Next.js – Content in the CMS (posts, authors, categories) is the raw material for a knowledge base.
- How to Use Webhooks in a Headless CMS (Laravel Example) – Trigger re-indexing when content changes so your RAG index stays in sync.
Frequently Asked Questions
What is RAG and why use it with a CMS?
RAG (Retrieval-Augmented Generation) is a pattern where you give an AI model relevant documents before asking it to answer a question. Using your headless CMS as the source means answers are grounded in your actual product content—FAQs, docs, tutorials, changelogs—instead of the model guessing.
Which CMS content is best for RAG?
Structured, published content with clear headings: FAQs, product docs, feature pages, tutorials, and changelogs. Avoid drafts, PII, and legal content that must be exact.
How do I keep the RAG index up to date?
Use webhooks. When content is published or updated in the CMS, fire a webhook that triggers re-chunking, re-embedding, and upserting into your vector store. Store a version or updatedAt to make the process idempotent.
What chunking strategy works best for CMS content?
Split by headings (H2/H3), keep chunks around 200–800 tokens, and include the document title and section heading as context in each chunk. For FAQs, use one Q/A per chunk.
Can I use RAG with any headless CMS?
Yes. Any CMS with a REST or GraphQL API can serve as the source. The pipeline is: export published entries via the API, normalize to text, chunk, embed, and index. ElmapiCMS, Strapi, Directus, Sanity, and Contentful all work.