AI Search & Retrieval Insights

This section contains technical analyses, research-backed explanations, and system-level insights into how AI search and answer engines extract, evaluate, and cite information.

Knowledge Base: GEO Training

Key Research Areas: AI Search, Retrieval Mechanics, and Citation Analysis

AI Search Research
Technical analyses of how AI search systems (ChatGPT, Perplexity, Google AI Overviews) retrieve, evaluate, and cite web content. This research covers retrieval mechanics, citation behavior, grounding budgets, entity resolution, and trust scoring in AI-mediated search environments.
Retrieval Mechanics
The technical processes by which AI systems extract, chunk, prioritize, and ground content from web sources. This includes semantic retrieval, query-document matching, relevance scoring, content compression, and citation anchor identification. Understanding retrieval mechanics enables optimization for AI search visibility and citation accuracy.
Citation Analysis
Analysis of how AI systems cite and attribute web content in their responses. This research examines citation patterns, source selection criteria, attribution accuracy, and citation suppression factors. Citation analysis reveals why some content is cited frequently while other content is ignored, regardless of traditional SEO rankings.

Featured Analysis

Semantic Queries & Query Optimization

Explains how semantic relationships collapse query complexity and reduce time to value. Technical breakdown of how traditional SQL queries with dozens of JOINs become concise, relationship-aware logic.

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Performance & Caching Insights

Explains intelligent pushdown optimization, query performance tuning, and caching engines that reduce compute spend while maintaining query speed and accuracy.

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Data Virtualization Best Practices

Explains how to connect every source into a semantic, virtualized layer with no ingestion or duplication. Covers automatic mapping, federated queries, and unified graph views.

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Technical Breakdowns

Enterprise LLM Foundation

Explains how to build reliable AI workflows on structured understanding. Technical analysis of structured semantic context, verified relationships, and virtualized access for trustworthy LLM operations.

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Knowledge Graph Exploration

Explains interactive knowledge graph techniques for traversing relationships, surfacing insights, and generating SQL or natural-language queries automatically.

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Research & Systems

Technical analyses spanning multiple domains within AI search and retrieval systems, from extraction mechanics to citation behavior.

Semantic Layer Architecture

SQL-native ontologies, reusable logic, metrics, hierarchies, and automated reasoning across knowledge graphs.

Data Virtualization

Federated queries, intelligent pushdown, caching strategies, and unified graph views across all data sources.

AI Workflow Optimization

Building reliable LLM workflows, GraphRAG implementation, NL2SQL generation, and structured semantic context for AI.

Query Performance

Semantic query optimization, relationship-aware logic, query complexity reduction, and performance tuning strategies.

Additional Insights

Related Research & Resources

Explore related research areas and implementation resources:

Generative Engine Optimization AI Search Diagnostics Training Implementation Support