LLM Accuracy Drift: Q1 2026 Observations
We tracked factual accuracy of four major LLMs answering company-specific questions over 90 days. Accuracy for companies without structured profiles degraded by 12% on average, while companies with verified profiles maintained stable accuracy.
Methodology
- 500 company-specific questions tested weekly across GPT-4o, Claude, Gemini, and Llama 3.
- Answers graded against verified company data on a 5-point accuracy scale.
- Companies split into two cohorts: with and without AuthorityPrompt profiles.
Findings
- Companies with verified profiles: 4.1/5.0 average accuracy (stable).
- Companies without profiles: 3.6/5.0 → 3.2/5.0 over 90 days (declining).
- Biggest drift areas: funding data, employee counts, product descriptions.
Related signals
Other tracked signals in this area.
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- AI Search Market Share: Q1 2026 Analysis — AI-powered search tools now handle 28% of all informational queries globally, up from 12% in Q1 2025. The shift from traditional search to A
- AI Visibility Signals: 2026 Archive — All AI visibility signals from 2026 — tracking changes in AI behavior, LLM updates, regulatory developments, and industry trends that affect
- Enterprise RAG Spending to Reach $12B in 2026 — Enterprise spending on Retrieval-Augmented Generation (RAG) infrastructure is projected to reach $12 billion in 2026, a 3.5x increase from 2
- AI Content Detection Is Reshaping Trust Signals — AI content detectors are being integrated into search engines and LLM pipelines. Human-verified, source-attributed content now receives meas
- See all in Signals
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.