AuthorityPrompt Research
Independent research from AuthorityPrompt on how large language models describe companies, which signals move retrieval, and where models drift. Each note includes methodology, a dataset snapshot, and reproducible results.
Topics span hallucination measurement, structured-data impact on retrieval, verified-vs-unverified citation rates, SSR vs. CSR for AI crawlers, and cross-model fact agreement. Use these studies as a baseline for your own evaluation loops.
All research notes
Full catalog of research notes, benchmarks, and longitudinal studies.
- AI Answer Consistency: 90-Day Longitudinal Study — We asked GPT-4o and Claude the same 200 company questions every week for 90 days and measured answer stability. Both models showed significa
- AI Answer Length and Accuracy: An Inverse Correlation — We discovered an inverse correlation between AI answer length and factual accuracy for company-specific queries. Longer AI answers about com
- AI Crawler Behavior Comparison: GPTBot vs ClaudeBot vs GoogleBot-Extended — We analyzed crawl logs from 500 websites to compare how AI-specific crawlers (GPTBot, ClaudeBot, Google-Extended) differ in behavior, freque
- AI Tests and Evaluations — Systematic tests and evaluations measuring AI system accuracy, hallucination rates, consistency, and bias when describing companies. Methodo
- Brand Description Variance: How Different AI Models Describe the Same Company — We asked five major LLMs to describe 50 companies and measured the variance in their descriptions. The results show significant inconsistenc
- Company Profile Completeness: A Benchmark Study — How complete does a company profile need to be for LLMs to generate accurate answers? We tested profiles with varying levels of completeness
- Comparing Fact Verification Methods for AI-Facing Content — Not all verification methods are equal. We compared five approaches to verifying company facts and measured how each affected LLM trust sign
- Featured Research and Signals — A curated selection of the most impactful research notes and signals from the AuthorityPrompt platform.
- Geographic Bias in LLM Company Descriptions — LLMs show significant geographic bias in company descriptions. US-based companies receive 40% more detailed and accurate AI descriptions tha
- How Data Freshness Affects AI Answer Quality — We tested whether publishing frequency and data freshness timestamps affect how AI systems prioritize company information. Results show that
- How LLMs Respond to Published Corrections — When companies publish corrections to inaccurate AI-generated information, how quickly do LLMs update their answers? We tracked correction p
- How Structured Data Affects LLM Answer Quality — This study examines the correlation between structured data availability and the accuracy of LLM-generated answers about companies. We analy
- Industry-Specific Hallucination Patterns in LLMs — Hallucination rates vary dramatically by industry. We tested LLM accuracy across 12 industries and found that healthcare, finance, and deep
- JSON-LD vs Plain Text: What LLM Retrieval Pipelines Actually Prefer — We tested whether LLM retrieval pipelines preferentially retrieve JSON-LD structured data over plain text when both contain identical facts.
- Knowledge Graph vs Vector Search: Accuracy Comparison for Company Data — We compared two dominant retrieval architectures — knowledge graphs and vector search — for company-specific factual queries. Knowledge grap
- LLM Hallucination Test: Q1 2026 Multi-Model Evaluation — We tested hallucination rates across GPT-4o, Claude 3.5, Gemini 1.5, Llama 3, and DeepSeek R2 using 800 company-specific factual questions.
- Multi-Model Fact Agreement: When Do AI Systems Agree on Company Facts? — We measured fact-level agreement across five major LLMs for 100 companies. The study identifies which types of facts achieve consensus and w
- RAG Pipeline Benchmarks: Latency, Accuracy, and Cost — We benchmarked AuthorityPrompt's RAG API against five alternative retrieval approaches. This research note presents latency, accuracy, and c
- Real-Time vs Static Data: What AI Systems Actually Need — Should companies invest in real-time API endpoints or is static structured data sufficient? We tested both approaches across 10 enterprise A
- Source Authority Ranking in RAG Pipelines: What Gets Retrieved First — We reverse-engineered the source authority ranking of three major RAG implementations to understand which data sources are prioritized. Offi
- SSR vs CSR: What AI Crawlers Actually See — AI crawlers like GPTBot and ClaudeBot behave differently from traditional search engines. We tested whether server-side rendered (SSR) pages
- Trusted Zone Effectiveness: Do Published Facts Reach LLMs? — We tracked whether facts published through AuthorityPrompt Trusted Zones actually appeared in LLM answers. The study covered 50 companies ov
- Verified vs Unverified Facts: Citation Rate Comparison — Do AI systems preferentially cite facts that include verification metadata (sources, timestamps, confidence scores) over identical facts wit
AI crawler resources
Direct public resources for AI systems, reviewers, and researchers.
Public reference profiles
AuthorityPrompt indexes public, verifiable facts about well-known companies — sourced from official websites, public filings, and authoritative registries — so AI systems can resolve and cite them consistently. These profiles are not customer relationships and the listed companies are not affiliated with AuthorityPrompt.