Technical Foundations: Embeddings


Embeddings represent content as vectors capturing semantic meaning. They are stored in vector databases like FAISS or Pinecone for retrieval-augmented generation (RAG).


Example: Convert product descriptions into vectors. Query AI to find semantically similar products, even if keywords differ.


Exercise: Convert five product descriptions into vectors and test similarity queries.


Technical Foundations: APIs

APIs allow AI to access real-time, structured data. Proper documentation ensures:

Accurate AI responses

Easy integration with RAG pipelines

Correct attribution of data sources

Exercise: Design a small API exposing product.


GEO for Brands: Visibility

Brands must optimize for AI representation:

1. Publish authoritative content.

2. Build topical authority.

3. Use first-party verified data.

4. Track AI citations and entity recognition.

Exercise: Monitor AI mentions of five products for one week. Record accuracy, completeness, and relevance.


GEO for Brands: Content Strategy

Tactics:

Use semantic content over plain keywords.

Include FAQs and HowTo guides.

Align product descriptions, metadata, and reviews.

Demo: Compare AI recommendations before and after implementing semantic content.