Retrieval Interleaved Generation (RIG)

Standard RAG retrieves documents before generation and injects them into the context window — a "retrieve-then-generate" approach. This works well for short, focused queries, but breaks down for long-form outputs where the model makes dozens of distinct factual claims across multiple topics. By the time generation reaches the fifth paragraph, the pre-retrieved context may be irrelevant to the specific claim being generated, and the model falls back on parametric knowledge — which may be outdated, incorrect, or hallucinated.

RIG addresses this by integrating retrieval into the generation loop. The model generates text until it recognizes a point where external grounding would improve accuracy — a specific fact, a statistic, a name, a date — then issues a retrieval query, incorporates the result, and continues generation. This interleaved approach means each factual claim in the output can be independently grounded in retrieved evidence, dramatically reducing hallucination rates for complex, multi-topic outputs. Google's implementation in Gemini, for instance, dynamically queries Google Search during generation to ground factual statements in real-time web results.

The enterprise applications are highest-value in document generation workflows: automated research reports that cite accurate, current data; due diligence summaries that ground every claim in retrieved financial documents; compliance reports that reference specific regulation text at each relevant statement; and clinical documentation that retrieves relevant clinical guidelines during generation. Compared to standard RAG, RIG typically requires more LLM calls per generation (increasing latency and cost), but the accuracy improvement on long-form factual tasks justifies the investment for high-stakes content.