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ChatGPT, BingChat, LaMDA, LLaMa, PaLM - the rise and rise of AI-based Large Language Models & their free alternatives visualized in one chart.

ChatGPT, BingChat, LaMDA, LLaMa, PaLM - the rise and rise of AI-based Large Language Models & their free alternatives visualized in one chart.

ChatGPT, BingChat, LaMDA, LLaMa, PaLM - the rise and rise of AI-based Large Language Models & their free alternatives visualized in one chart.

ChatGPT, BingChat, LaMDA, LLaMa, PaLM - the rise and rise of AI-based Large Language Models & their free alternatives visualized in one chart.

ChatGPT's US mobile share fell to ~22% in March 2026 (down from ~31% YoY); Q1 installs dropped ~28% YoY while Bing Chat and Perplexity grew (Sensor Tower via Seeking Alpha, Apr 4, 2026).

ChatGPT, BingChat, LaMDA, LLaMa, PaLM - the rise and rise of AI-based Large Language Models & their free alternatives visualized in one chart.

ChatGPT, BingChat, LaMDA, LLaMa, PaLM - the rise and rise of AI-based Large Language Models & their free alternatives visualized in one chart.

ChatGPT, BingChat, LaMDA, LLaMa, PaLM - the rise and rise of AI-based Large Language Models & their free alternatives visualized in one chart.

Generative Search Engines (GSEs), powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), are reshaping information retrieval. While commercial systems (e.g., BingChat, Perplexity.ai) demonstrate impressive semantic synthesis capabilities, their black-box nature fundamentally undermines established Search Engine Optimization (SEO) practices. Content creators face a critical challenge: their optimization strategies, effective in traditional search engines, are misaligned with generative retrieval contexts, resulting in diminished visibility. To bridge this gap, we propose a Role-Augmented Intent-Driven Generative Search Engine Optimization (G-SEO) method, providing a structured optimization pathway tailored for GSE scenarios. Our method models search intent through reflective refinement across diverse informational roles, enabling targeted content enhancement. To better evaluate the method under realistic settings, we address the benchmarking limitations of prior work by: (1) extending the GEO dataset with diversified query variations reflecting real-world search scenarios and (2) introducing G-Eval 2.0, a 6-level LLM-augmented evaluation rubric for fine-grained human-aligned assessment. Experimental results demonstrate that search intent serves as an effective signal for guiding content optimization, yielding significant improvements over single-aspect baseline approaches in both subjective impressions and objective content visibility within GSE responses.

YouTube video by Global AI Community

YouTube video by Global AI Community