Traditional SEO emphasizes visibility in search results using keywords, link building, and page authority. GEO, however, prioritizes AI readability and semantic comprehension. Instead of relying solely on ranking, it focuses on structured data, entity representation, and AI-consumable knowledge.

Key Concepts:

1. Semantic Entities: Identify core entities (products, brands, categories).

2. Relationship Mapping: Define connections between entities.

3. Structured Content: Schema markup, FAQs, product attributes.

4. Contextual Accuracy: Ensure AI outputs are factually correct.


Example:

SEO Keyword: “best matte lipstick Bangladesh”

GEO Mapping:

Product Type: Lipstick

Finish: Matte

Brand: Lafz

Origin: Italy

Reviews: Positive

Exercise: Create a table mapping five products using GEO principles.


Understanding Generative Engines:


Generative engines rely on large language models (LLMs), trained on massive datasets. They can predict text sequences, synthesize information, and provide human-like responses.

Components:

Training Data: Licensed datasets, proprietary content.


Model Architecture: Transformers for sequence learning.


Prompt Interpretation: Understanding intent behind queries.


Context Retrieval: Fetching relevant facts and entities.


Output Generation: Producing coherent, context-aware text.


Demo: Query: “Recommend a skincare routine for humid weather in Dhaka.”


AI generates a step-by-step routine considering climate, skin type, and product compatibility


Exercise: Compare AI outputs for five different skincare queries and evaluate relevance, accuracy, and contextual understanding.



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