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
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.