Building AI-Powered Apps with Angular

9

Implementing Vector Search with RAG and Firestore

While LLMs are extraordinarily powerful in understanding and generating natural language, they inherently have limitations: their knowledge is confined to the corpus of data on which they were trained, which makes them susceptible to "hallucinations" (generating false but plausible information) and incapable of accessing recent or domain-specific data without costly and prolonged retraining.

This is where Retrieval Augmented Generation (RAG) comes in, a revolutionary technique that aims to overcome these barriers by enabling LLMs to access, retrieve, and incorporate external, up-to-date, and domain-specific information into their responses. This significantly reduces hallucinations and grounds ...