Beyond SEO: A Transformer-Based Approach for Reinventing Web Content Optimisation

TL;DR

This paper introduces a transformer-based method for web content optimization tailored for AI-driven search engines, demonstrating significant improvements in content visibility through domain-specific fine-tuning of a BART model.

Contribution

It presents the first empirical evidence that domain-specific fine-tuning of transformers can enhance web content discoverability in generative search environments.

Findings

Significant improvements in ROUGE-L and BLEU scores over baseline models.

30.96% increase in position-adjusted word count metrics.

Demonstrated effectiveness of small-scale fine-tuning for content visibility enhancement.

Abstract

The rise of generative AI search engines is disrupting traditional SEO, with Gartner predicting 25% reduction in conventional search usage by 2026. This necessitates new approaches for web content visibility in AI-driven search environments. We present a domain-specific fine-tuning approach for Generative Engine Optimization (GEO) that transforms web content to improve discoverability in large language model outputs. Our method fine-tunes a BART-base transformer on synthetically generated training data comprising 1,905 cleaned travel website content pairs. Each pair consists of raw website text and its GEO-optimized counterpart incorporating credible citations, statistical evidence, and improved linguistic fluency. We evaluate using intrinsic metrics (ROUGE-L, BLEU) and extrinsic visibility assessments through controlled experiments with Llama-3.3-70B. The fine-tuned model achieves significant improvements over baseline BART: ROUGE-L scores of 0.249 (vs. 0.226) and BLEU scores of 0.200 (vs. 0.173). Most importantly, optimized content demonstrates substantial visibility gains in generative search responses with 15.63% improvement in absolute word count and 30.96% improvement in position-adjusted word count metrics. This work provides the first empirical demonstration that targeted transformer fine-tuning can effectively enhance web content visibility in generative search engines with modest computational resources. Our results suggest GEO represents a tractable approach for content optimization in the AI-driven search landscape, offering concrete evidence that small-scale, domain-focused fine-tuning yields meaningful improvements in content discoverability.