AI Experience Optimisation (AEO), Generative Experience Optimisation (GEO), and the future of search.

Generative AI has the potential to transform search engine optimisation (SEO) in several ways:

- Content Creation: It can automate the process of producing high-quality, keyword-rich content, helping websites to quickly produce optimised blog posts, product descriptions, and other materials.
- Personalisation: Generative AI can tailor content to a specific audience based on preferences and past behaviours, ultimately leading to improved user engagement.
- Content optimisation: It can help identify the most effective keywords, meta descriptions, and headings by analysing search trends and competition, making content more relevant and visible to the audience.
- Efficiency: By automating repetitive content creation tasks, SEO specialists can focus more on strategy and data analysis, improving overall effectiveness.

The key difference between generative AI and general AI lies in their functions:

- Generative AI: Focuses on creating new, original content. It uses advanced models like GPT (Generative Pre-trained Transformer) or GAN (Generative Adversarial Network) to produce text, images, and other data that closely resemble real-world examples.
- General AI: Refers to a broader category of AI that includes systems designed for analysis, classification, prediction, and other tasks. General AI encompasses machine learning algorithms that can solve a variety of problems, including data analysis, natural language processing, recommendation systems, and computer vision.

Google's Search Generative Experience (SGE) or AI Overview is a new way of responding to user searches by combining the power of large language models and knowledge graphs.

Rather than relying entirely on data indexed and ranked by Google, the AI Overview makes use of LLMs and AI to satisfy a users search query.