Strategic Planning
The New Search Stack: A Plain-English Guide to Chunking, Embeddings, Vector DBs, RAG, AEO, LLMO, GEO and Multimodal Indexing
Why these terms matter now and what brands need to do about them.
8 December 2025
9 min read
AI search has introduced a completely new vocabulary. If you work in eCommerce, SEO, or retail media, you’ve probably heard terms like chunking, embeddings, vector databases, RAG, AEO, and now GEO and LLMO. And if you’re like most people, you’ve also thought: Do I actually need to understand any of this?
Short answer: yes because this is the technical spine of how AI search engines like ChatGPT, Perplexity, Google, and Amazon’s Rufus choose which brands appear in their answers.
Here’s a simple breakdown of each term and what it means for consumer brands.
Chunking
LLMs can’t index full websites or long documents in one go. Chunking solves this by breaking your content into smaller, meaningful sections often a few hundred words each.
Why it matters:
* Better chunking = higher chance your content is retrieved by an AI model.
* Poor chunking = you lose visibility in AI search because your answers become “unreadable” to the system.
For brands: Your product pages, guides, FAQs, reviews and manuals should be structured in clear, semantically tight blocks.
Embedding
An embedding is a numerical fingerprint of meaning.
When text or an image is converted into an embedding, an AI model can compare them by meaning, not keywords.
Why it matters:
AI search doesn’t work on keywords. It works on meaning. Embeddings are the bridge.
For brands: Your content needs to be structured and explicit so embeddings capture the right signals.
Vector DB
A vector database stores embeddings and makes them searchable.
Instead of matching words, it matches meaning by locating the closest vectors.
Why it matters:
This is how AI systems “remember” and “retrieve” information to answer queries.
For brands: When platforms store your product data in vector form, clean structured content becomes a competitive advantage.
RAG (Retrieval-Augmented Generation)
RAG means the model retrieves relevant information from a database before generating its answer.
Why it matters:
* RAG is now everywhere: ChatGPT search, Perplexity, Shopify’s AI system, Amazon Q, enterprise bots.
* It increases accuracy and reduces hallucinations.
For brands: If your content isn’t retrievable, it will never appear in an AI answer.
AEO (Answer Engine Optimisation)
Think of AEO as SEO for AI-powered assistants.
Instead of optimising for rankings, you optimise for being the source an AI chooses when answering the question.
Why it matters:
* AI assistants decide what a consumer sees first.
* The “AI shelf” becomes the new category battleground.
For brands: AEO means writing clear, factual, structured content that answers questions directly the opposite of fluffy SEO copy.
LLMO (Large Language Model Optimisation)
LLMO goes a step further than AEO. It focuses on how your brand appears inside LLMs themselves.
It includes:
* Your brand’s share of mentions
* Your placement in generated shortlists
* How models interpret your claims
* Whether you appear in “best of” or “top X” answer formats
Why it matters:
LLMs are becoming the primary discovery engine for many consumers. LLMO measures and improves your visibility inside them, similar to what Share-of-Model tools track.
For brands: This is the next competitive metric after SEO, ROAS, and market share.
GEO (Generative Engine Optimisation)
GEO is the umbrella term for optimising content for generative systems:
* AI search engines
* Chat assistants
* Generative rankings and recommendations
* AI-powered shopping experiences (Rufus, Perplexity, Shopify Magic)
Why it matters:
This is no longer theory. Rufus already shapes Amazon’s search journey, and every major platform is rolling out generative assistants.
For brands: GEO becomes a core marketing capability. If you’re not optimised for generative engines, you’re invisible in the next wave of consumer search.
Multimodal Indexing
Modern search systems don’t just read text. They process:
* Images
* Video
* Audio
* 3D representations
* Structured data
* Reviews
* Specs
* Product taxonomies
Why it matters:
AI engines create a “multimodal profile” of each product, which influences relevance and ranking.
For brands: Every asset should be consistent and enriched with factual signals. Your imagery matters as much as your text.
How These Concepts Fit Together
Here’s a simple flow:
1. Chunking breaks your content up
2. Embeddings turn each chunk into meaning-based vectors
3. Vector DBs store these vectors
4. RAG retrieves the right chunks at answer time
5. AEO / LLMO / GEO influence which brands are selected
6. Multimodal indexing enriches the model’s understanding of your product universe
Together, they decide whether your brand appears to a consumer who simply asks:
> “What’s the best sunscreen for long-distance cycling?”
> “What’s a good work boot for winter construction?”
> “Which electrolytes are best for marathon runners?”
This is how the new search stack works and why brands must adapt immediately.
Short answer: yes because this is the technical spine of how AI search engines like ChatGPT, Perplexity, Google, and Amazon’s Rufus choose which brands appear in their answers.
Here’s a simple breakdown of each term and what it means for consumer brands.
Chunking
LLMs can’t index full websites or long documents in one go. Chunking solves this by breaking your content into smaller, meaningful sections often a few hundred words each.
Why it matters:
* Better chunking = higher chance your content is retrieved by an AI model.
* Poor chunking = you lose visibility in AI search because your answers become “unreadable” to the system.
For brands: Your product pages, guides, FAQs, reviews and manuals should be structured in clear, semantically tight blocks.
Embedding
An embedding is a numerical fingerprint of meaning.
When text or an image is converted into an embedding, an AI model can compare them by meaning, not keywords.
Why it matters:
AI search doesn’t work on keywords. It works on meaning. Embeddings are the bridge.
For brands: Your content needs to be structured and explicit so embeddings capture the right signals.
Vector DB
A vector database stores embeddings and makes them searchable.
Instead of matching words, it matches meaning by locating the closest vectors.
Why it matters:
This is how AI systems “remember” and “retrieve” information to answer queries.
For brands: When platforms store your product data in vector form, clean structured content becomes a competitive advantage.
RAG (Retrieval-Augmented Generation)
RAG means the model retrieves relevant information from a database before generating its answer.
Why it matters:
* RAG is now everywhere: ChatGPT search, Perplexity, Shopify’s AI system, Amazon Q, enterprise bots.
* It increases accuracy and reduces hallucinations.
For brands: If your content isn’t retrievable, it will never appear in an AI answer.
AEO (Answer Engine Optimisation)
Think of AEO as SEO for AI-powered assistants.
Instead of optimising for rankings, you optimise for being the source an AI chooses when answering the question.
Why it matters:
* AI assistants decide what a consumer sees first.
* The “AI shelf” becomes the new category battleground.
For brands: AEO means writing clear, factual, structured content that answers questions directly the opposite of fluffy SEO copy.
LLMO (Large Language Model Optimisation)
LLMO goes a step further than AEO. It focuses on how your brand appears inside LLMs themselves.
It includes:
* Your brand’s share of mentions
* Your placement in generated shortlists
* How models interpret your claims
* Whether you appear in “best of” or “top X” answer formats
Why it matters:
LLMs are becoming the primary discovery engine for many consumers. LLMO measures and improves your visibility inside them, similar to what Share-of-Model tools track.
For brands: This is the next competitive metric after SEO, ROAS, and market share.
GEO (Generative Engine Optimisation)
GEO is the umbrella term for optimising content for generative systems:
* AI search engines
* Chat assistants
* Generative rankings and recommendations
* AI-powered shopping experiences (Rufus, Perplexity, Shopify Magic)
Why it matters:
This is no longer theory. Rufus already shapes Amazon’s search journey, and every major platform is rolling out generative assistants.
For brands: GEO becomes a core marketing capability. If you’re not optimised for generative engines, you’re invisible in the next wave of consumer search.
Multimodal Indexing
Modern search systems don’t just read text. They process:
* Images
* Video
* Audio
* 3D representations
* Structured data
* Reviews
* Specs
* Product taxonomies
Why it matters:
AI engines create a “multimodal profile” of each product, which influences relevance and ranking.
For brands: Every asset should be consistent and enriched with factual signals. Your imagery matters as much as your text.
How These Concepts Fit Together
Here’s a simple flow:
1. Chunking breaks your content up
2. Embeddings turn each chunk into meaning-based vectors
3. Vector DBs store these vectors
4. RAG retrieves the right chunks at answer time
5. AEO / LLMO / GEO influence which brands are selected
6. Multimodal indexing enriches the model’s understanding of your product universe
Together, they decide whether your brand appears to a consumer who simply asks:
> “What’s the best sunscreen for long-distance cycling?”
> “What’s a good work boot for winter construction?”
> “Which electrolytes are best for marathon runners?”
This is how the new search stack works and why brands must adapt immediately.