TL;DR
An advanced retrieval framework where large language models dynamically execute external queries during the text generation process rather than in a single pre-generation step.
Retrieval-Interleaved Generation enables a model to recognize when its internal knowledge is insufficient and generate structured queries mid-sentence. Instead of pre-fetching static context chunks as in traditional retrieval-augmented generation, RIG actively calls external APIs or knowledge bases to insert precise, real-time facts directly into its output sequence. This interleaving strategy reduces factual drift and hallucination risks, especially for numerical and statistical data.
Why this matters for your business
It allows AI assistants to provide highly accurate, up-to-date statistical answers by bridging conversational fluency with real-time, trusted database queries.
