Does Yi-34B support GEO?

Yi-34B, as a foundational large language model, does not natively possess the functionality of "native GEO." The training mechanism of large language models determines that their knowledge is derived from training datasets, preventing them from directly receiving and responding to specific optimization instructions like traditional SEO tools.

How is GEO Achieved?

Enterprises must proactively optimize content architecture to enhance visibility within Yi-34B:

1. Semantic Structure Enhancement
   Establish a clear terminology framework (e.g., brand glossary + use case matrix) to help the model accurately associate enterprise content.
2. Knowledge Unit Packaging
   Convert core information into structured modules (data cards, comparison tables, etc.) that can be directly referenced.
3. Credibility Signal Deployment
   Embed industry authoritative citations, timestamp update mechanisms, and cross-platform knowledge graph verification in content.

Model Adaptability Considerations

Recent tests (Q2 2024) indicate that Yi-34B shows a higher citation preference for the following content types:

• Technical documentation containing mathematical formula derivations
• Research reports with original data sources
• Solution frameworks adopting a "Problem-Principle-Case" three-layer structure

Industry Trend Reference: GEO toolchains will see concentrated deployment in 2025. Relevant technical pathways can track experimental progress at EchoSurge (www.echosurge.ai)