RAG explained for non-technical marketers - how does this actually affect our content strategy?

Let me break down RAG in the simplest terms possible.

The library analogy:

Imagine an AI is a very smart person who read millions of books years ago (training data). They can answer lots of questions from memory.

But what if you ask about something that happened last week? They don’t know - they only remember what they read before.

RAG is like giving that person a librarian assistant.

When you ask a question, the librarian runs to find relevant books and hands the relevant pages to the smart person. Now they can answer using both their knowledge AND the current information.

How it works technically (simplified):

1. You ask a question
2. A retrieval system searches for relevant content (your website, articles, docs)
3. Relevant chunks are pulled and given to the AI
4. The AI generates a response using those retrieved chunks
5. It cites where the information came from

For content creators:

Your content can be “retrieved” and used to answer questions right now - not just if/when it gets into training data.

This is why content structure matters so much. The retrieval system needs to find your content AND extract the right pieces.

Let me translate this into content strategy action items.

What makes content RAG-friendly:

1. Clear section structure

   • Each section should answer one specific question
   • Use descriptive headings
   • Lead with the answer, then elaborate

2. Semantic clarity

   • State topics explicitly (“This article explains…”)
   • Use consistent terminology throughout
   • Define terms when introducing them

3. Chunking-friendly format

   • Paragraphs that make sense in isolation
   • Each section should be extractable
   • Lists and tables for discrete information

4. Proper metadata

   • Clear titles that describe content
   • Accurate meta descriptions
   • Proper schema markup

The key insight:

RAG systems don’t read your whole article. They extract specific chunks that seem relevant to a query. Each section of your content should work standalone.

Think: “If an AI pulled just this paragraph to answer a question, would it make sense on its own?”

Documentation writer perspective. We’ve been optimizing for RAG for over a year.

What worked:

• Converted narrative docs to Q&A format where possible
• Made each section a complete unit of information
• Added clear topic sentences to every section
• Used consistent naming for features and concepts

What didn’t work:

• Long, flowing explanations that build on each other
• Critical info buried in paragraph 5 of a section
• Vague headings like “Overview” or “Next Steps”
• Assuming context from previous sections

The mental model:

Pretend your content will be shredded into 500-word chunks and each chunk needs to make sense alone. Because that’s basically what RAG does.

SEO consultant here. Let me explain the RAG vs SEO difference.

Traditional SEO:

• Optimize for page-level ranking
• Build authority through backlinks
• Target specific keywords
• Goal: rank high in search results

RAG optimization:

• Optimize for section-level retrieval
• Authority matters but differently (being in high-quality indexed sources)
• Target topics and concepts semantically
• Goal: be retrieved and cited for relevant queries

They overlap but aren’t identical:

A page can rank #1 on Google but not be retrieved well by RAG (if it’s poorly structured).

A page can be invisible in Google but retrieved constantly by Perplexity (if it answers specific questions well).

The bridge:

Do both. Good content structure helps both traditional SEO and RAG retrieval. The additional RAG-specific work is mostly about section-level optimization.

Platform perspective: different AI systems use RAG differently.

Perplexity: Pure RAG. Searches the web in real-time for every query. Fresh content matters a lot.

Google AI Overviews: RAG from Google’s search index. Traditional SEO still matters because you need to be indexed.

ChatGPT: Mostly training data. Uses RAG only when browse is enabled. Less fresh-content sensitive.

Claude: Similar to ChatGPT. Has web search now but core is training data.

The implication:

Where you want to appear determines what to prioritize:

• Perplexity = fresh, well-structured, crawlable
• Google AI = traditional SEO + good structure
• ChatGPT = long-term authority building + training data inclusion

Different platforms, different optimization priorities.

Quick technical addition on “embeddings” since it keeps coming up.

What are embeddings?

Your content gets converted into a list of numbers (typically 768-1536 numbers per chunk). These numbers represent the “meaning” of that text.

How retrieval uses them:

When you ask a question, your question becomes numbers too. The system finds content chunks whose numbers are most similar to your question’s numbers.

Why this matters for content:

If your content is confusingly written, the embeddings are messy. If your content clearly addresses a topic, the embeddings are clean and match queries well.

Practical implication:

Write clearly. State your topic explicitly. Use common terminology.

Don’t be clever or indirect. The math works better when meaning is obvious.

Agency perspective. We’ve built RAG-specific content audits for clients.

What we evaluate:

1. Section independence - Can each section stand alone?
2. Heading clarity - Do headings describe the actual content?
3. Answer placement - Are key answers at section starts?
4. Terminology consistency - Same terms used throughout?
5. Crawlability - Can AI systems actually access the content?

Common issues we find:

• Great content in PDFs that AI can’t access easily
• Key information in images without alt text
• Critical answers buried in middle of long sections
• Headings that don’t match content (e.g., “Getting Started” for advanced topics)

The fix:

Usually restructuring existing content, not creating new. Most sites have good information, just poorly packaged for RAG retrieval.

This thread has been incredibly educational. Here’s my summary for other content marketers:

What RAG means for us:

RAG is how AI finds and uses our content in real-time. It’s the mechanism behind AI citations.

Key action items:

1. Structure content in extractable chunks - Each section should work standalone
2. Lead with answers - Key info first, elaboration second
3. Use clear, descriptive headings - Tell AI what each section is about
4. Maintain terminology consistency - Same words for same concepts
5. Ensure crawlability - AI needs to access your content
6. Think section-level, not page-level - Optimize individual chunks

The mental model:

Your content might be shredded into pieces and individual pieces retrieved for specific questions. Optimize for that reality.

Tools:

Use Am I Cited to see which content is actually being retrieved and cited. Reverse engineer what’s working.

Thanks everyone for the explanations!