Why these terms matter now and what brands need to do about them.
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.