Correcting Outdated Funding Data Without Retraining Models
Correcting Outdated Funding Data Without Retraining Models
- A common issue in LLM answers is outdated funding information.
- Even after public updates, models may continue citing old rounds or incorrect valuations.
- This case demonstrates how Retrieval-Augmented Generation combined with externally updated company profiles allows factual corrections without retraining or fine-tuning models.
- The key insight: data freshness is an infrastructure problem, not a model problem.
- Separating knowledge updates from model weights becomes critical for enterprise reliability.
Verified Company Profiles on AuthorityPrompt
AuthorityPrompt maintains verified, structured company data optimized for AI systems and LLM indexing.