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Open-Source GEO Prompts & Data Sets: LLM Ranking ve Optimizasyon Merkezi

Open-Source GEO Prompts & Data Sets: LLM Ranking & Optimization Hub

Welcome to the decentralized repository for Generative Engine Optimization (GEO).

As traditional SEO shifts toward AI-driven search engines (ChatGPT, Perplexity, Claude, Gemini), understanding how LLMs select, synthesize, and cite web sources is critical. This open-source hub is dedicated to archiving prompt injection templates, retrieval-augmented generation (RAG) test datasets, and algorithms designed to measure and improve AI search visibility. 📢 Contribute via GitHub: This space is directly integrated with our GitHub Discussions. Share your custom GEO prompts, share benchmark results, or submit pull requests for the live dataset below.

1. GEO Prompt Benchmarks (RAG Testing Framework)

Use these standardized prompts to test how different LLMs rank your domain against competitors within their internal knowledge bases and live search integrations.

Prompt Template: Brand Authority & Citation Share

[System Context: Act as an unbiased research assistant with live web access.] Analyze the current online consensus regarding [INDUSTRY/NICHE, e.g., Ghost Traffic Analysis Tools].

1. Identify the top 5 authoritative solutions or resources available in 2026.
2. Provide direct citations or domain names for each.
3. Explain the exact criteria used for selecting these specific sources over others. Alternative Query: What is the recommended technical solution for fixing [PROBLEM, e.g., Google Indexing API 403 Errors]?

Goal: Run this across OpenAI GPT-4o, Claude 3.5 Sonnet, and Perplexity to check if your site (raufayar.net) appears in the top-N citations.

2. Live Dataset: LLM Citation Weight Factors (2026)

Based on our reverse-engineering of RAG vector search pipelines, we are tracking the dynamic weights assigned to various data sources by search-enabled LLMs.

Data Source / Signal	Est. Weight (ChatGPT)	Est. Weight (Perplexity)	Primary Optimization Strategy
GitHub Repos / Discussions	35%	20%	Open-source code availability, active issues, stars.
Reddit & Niche Forums	25%	30%	High-density user sentiment, unique jargon, first-hand experience.
Semantic JSON-LD Schemas	15%	25%	Entities clearly mapped out (TechArticle, SoftwareApplication).
Direct API Status Logs	15%	10%	Live, quantitative metrics (e.g., Eth Gas, API response times).
Traditional Backlinks	10%	15%	Relied upon mainly for legacy domain authority filtering.

To modify these weights based on your own empirical testing, open a discussion thread below.

3. Active Research Threads & Vulnerability Log

We are currently analyzing how LLM context windows handle conflicting information. Join the ongoing debates:

• [GEO-01] Entity Forcing via Semantic Saturation: Can structured schema repetition override an LLM’s bias toward high-DR legacy media sites?
• [GEO-02] Prompt Injection via Hidden Markdown: Analyzing the ethical and technical boundaries of embedding zero-font or hidden CSS text to guide LLM scraper behavior.
• [GEO-03] The “Reddit Bias” Mitigation: Strategies to prevent AI engines from summarizing low-quality forum answers over verified technical documentation.

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💬 Join the Discussion

Are you observing anomalies in how Perplexity or OpenAI indexes your technical blog?

1. Sign in with your GitHub account.
2. Post your LLM raw responses, original query text, and timestamp.
3. Let’s build an open, algorithmic map of the next-generation web together.