Generative engine optimization poses unique challenges, and to gain insight, we’ve compiled experiences from a CTO and a CEO. They discuss everything from balancing content quality and diversity to ensuring content authenticity with human oversight. Here are the two expert perspectives on overcoming common hurdles in the realm of GEO.
One common challenge I’ve faced when implementing generative engine optimization (GEO) is balancing the quality and diversity of generated content. Initially, the models tended to produce either highly repetitive or overly varied outputs, leading to inconsistent quality.
To overcome this, I fine-tuned the model with a diverse yet focused training dataset, ensuring it included a range of examples within the desired output spectrum.
Additionally, I implemented temperature and top-k sampling techniques to better control the randomness and creativity of the responses. This approach allowed for a more consistent generation of high-quality content while maintaining the necessary diversity to avoid monotony.
Regular evaluation and iterative adjustments further refined the model’s performance, ensuring the generated content met the desired standards.
Dhari Alabdulhadi, CTO and Founder, Ubuy Netherlands
Ensuring Content Authenticity with Human Oversight
A common challenge with GEO is maintaining content authenticity. We’ve overcome this by integrating human oversight into the content-creation process. By combining AI efficiency with human creativity and insight, we ensure our content remains genuine and resonates with our audience.
Alex Stasiak, CEO & Founder, Startup House
