How to Build an Efficient Knowledge Base for AI Models
SMRTR summary
Building an effective AI knowledge base requires quality over quantity — major AI chatbots currently get nearly half of all queries wrong. A six-step process covering data collection, cleaning, indexing, storage, retrieval optimization, and regular updates keeps models accurate. Treating the knowledge base as a continuously refined asset, not a one-time data dump, separates reliable AI from one that guesses.
SMRTR provides this summary for quick context. The original article belongs to Daily.dev.
Read the original article