Blog - Geolyze

How to Make AI-Written Content Sound More Human

This article offers a practical walkthrough for transforming generic AI-generated text into natural, engaging writing. Using a clear case example focused on solar versus wind energy, it demonstrates how detailed prompting, iterative refinement, and model selection lead to more realistic and structured output. It also explores alternative AI tools, discusses common limitations such as hallucination, and outlines best practices for achieving tone, clarity, and emotional resonance in AI-assisted writing.

Dec 10, 2025•3 min read•19 views

From Search Results to Answers: AEO Explained

Search behavior is shifting from link-based results to direct, answer-focused responses delivered by search engines, voice assistants, and AI chat systems. Answer Engine Optimization (AEO) reflects this shift by preparing content so that machines can interpret it accurately and present it as a clear answer. This article explains how AEO works, how it relates to Generative Engine Optimization (GEO), and what strategies help ensure content remains visible in an increasingly answer-driven search environment.

Nov 18, 2025•5 min read•13 views

How AI Is Changing Search Visibility

AI systems are reshaping how people access information. Search experiences once dominated by ranked links are giving way to personalized prompts, synthesized answers, and AI-generated recommendations. As this transition accelerates, organizations need methods to assess and improve how often their content is retrieved, cited, and represented in AI-generated outputs. This article outlines the core concepts behind AI visibility and practical steps for improving it through AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization).

Dec 10, 2025•4 min read•13 views

How Does AI Mode’s Query Fan-Out Technique Work?

Query fan-out is an advanced information retrieval method that takes a single user query and expands it into multiple semantically related sub-queries. Instead of interpreting a query as a single intention, the system treats it as a possible collection of intentions—explicit, implicit, and contextual. By exploring these variations in parallel, the system can gather a significantly broader set of information before synthesizing a final answer.

Dec 10, 2025•4 min read•13 views

Black Hat GEO: Ethics and Risks in AI Search

This policy-focused analysis examines how black hat GEO tactics are evolving alongside AI-driven search systems. It highlights ethical risks related to synthetic authorship, misinformation, schema manipulation, and identity fabrication, using real examples such as AI adoption trends and the Sports Illustrated incident. The article also outlines the governance challenges facing regulators, platforms, and AI developers, emphasizing the need for transparency, accountability, strong verification standards, and human oversight in an era where LLMs increasingly shape what users see and trust.

Dec 10, 2025•4 min read•13 views