Summary

GEO offers content creators strategies to optimize website visibility within emerging generative search engines powered by LLMs. It addresses the challenge of reduced control over content placement in these new search paradigms, aiming to enhance discoverability and provide practical insights for both creators and search engine developers.

How It Works

The project introduces Generative Engine Optimization (GEO) techniques to boost a website's presence in LLM-driven search results. It leverages a custom dataset, GEO-BENCH, comprising diverse user queries and their associated search results, to empirically evaluate and demonstrate the effectiveness of these optimization methods.

Quick Start & Requirements

• Installation: Requires Python 3.9, managed via a conda environment (conda create -n geo python=3.9, conda activate geo), followed by pip install -r requirements.txt.
• Execution: Run experiments using cd src and python run_geo.py. Custom functions can be defined in src/geo_functions.py.
• Dataset: The GEO-BENCH dataset is available on Hugging Face (datasets.load_dataset('GEO-optim/geo-bench')).
• Leaderboard: An associated leaderboard is hosted at https://huggingface.co/spaces/Pranjal2041/GEO-bench.

Highlighted Details

• Demonstrates up to a 40% increase in source visibility within generative engine responses.
• Provides the GEO-BENCH dataset for reproducible evaluation of optimization techniques.

Maintenance & Community

The provided README does not contain information regarding project maintainers, community channels (e.g., Discord, Slack), or a public roadmap.

Licensing & Compatibility

No specific software license is mentioned in the README. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The README does not specify any known limitations, alpha status, unsupported platforms, or potential bugs. The focus appears to be on research replication.