What is LLMO vs. GEO—A Quick Analogy

This slide explains the meaning of LLMO and GEO, in contrast to AIO, which refers to the overall optimization for AI.

Transcript

1. Understanding LLMO and GEO Understanding LLMO and GEO  A Restaurant Recommendation Analogy Exploring how AI systems work by comparing them to human behavior when recommending restaurants Created by CoDigital, inc. utilizing Gemini and Manus.
2. The Question: Restaurant Recommendation Scenario  "Tell me about recommended restaurants near my house!"  When someone asks you this question, how do you respond? Your answer reveals different approaches to information retrieval - some based on existing knowledge, others requiring active search. This simple scenario helps us understand two fundamental concepts in AI: LLMO and GEO. Created by CoDigital, inc. utilizing Gemini and Manus.
3. Human Response: Using Known Information 1 Restaurants you've been to Places you have personal experience with and can confidently recommend based on your own visits. 2 Famous local restaurants Popular places you haven't visited personally but know are well-regarded in the local community. → This represents information already stored in your "brain" Created by CoDigital, inc. utilizing Gemini and Manus.
4. This is LLMO: Pre-stored Knowledge Retrieval  LLMO (Large Language Model) Information is already stored in the AI's "brain" and can be retrieved when needed, just like human memory. How it works: • The LLM has been trained on vast amounts of data • Knowledge is embedded within the model's parameters • When asked a question, it retrieves relevant information from its pre-trained knowledge • Similar to how you remember restaurants you've been to or heard about Created by CoDigital, inc. utilizing Gemini and Manus.
5. Human Response: Searching for New Information 3 When no suitable restaurant comes to mind... Most people will search online for well-reviewed restaurants and then make recommendations based on their findings. Google Yelp TripAdvisor → This represents actively searching for unknown information and incorporating it into your response Created by CoDigital, inc. utilizing Gemini and Manus.
6. This is GEO: Real-time Information Integration  GEO (Generation Engine) AI searches for unknown information in real-time and incorporates it into responses, just like searching online. How it works: • AI recognizes when it lacks specific information • Searches external databases or the internet in real-time • Retrieves relevant, up-to-date information • Integrates findings into the response • Similar to how you search Google when you don't know something Created by CoDigital, inc. utilizing Gemini and Manus.
7. LLMO vs GEO: A Comparison  LLMO  Uses information already learned during training  Fast response time - no external search needed  Like remembering restaurants you know  Limited to training data knowledge  May have outdated information VS  GEO  Searches for information in real-time  Slower response - requires external search  Like searching Google for restaurants  Access to current, up-to-date information  Can find information beyond training data Pre-stored Knowledge Retrieval Real-time Information Integration Created by CoDigital, inc. utilizing Gemini and Manus.
8. Conclusion Conclusion  Key Takeaway   LLMO Like human memory - retrieves information already stored in the AI's "brain" from training data  GEO Like online searching - actively finds new information in real- time to enhance responses Understanding LLMO and GEO helps us appreciate how AI systems work - sometimes drawing from learned knowledge, sometimes searching for new information, just like humans do when making recommendations. Created by CoDigital, inc. utilizing Gemini and Manus.