RIG vs RAG: what's the difference in artificial intelligence?
Article published in November 2024, updated in October 2026: origin of the RIG (Google's DataGemma), agentic RAG, place of the RAG in relation to connectors and the MCP.
RIG and RAG both combine information retrieval and AI generation, but their logic differs. In a Retrieval-Augmented Generation (RAG) approach, the system retrieves relevant context before generating its response. In a Retrieval-Interleaved Generation (RIG) approach, the model interrupts its generation to query a source at the precise moment it needs a fact, then resumes. RAG is suitable for many documentary uses; RIG is useful for ensuring the reliability of figures and specific facts throughout a response.
What is RAG in AI?
RAG, or Retrieval-Augmented Generation (Augmented generation by retrieval) improves generation by feeding the model information from external sources. The system first searches for relevant documents, adds them to the query context, and then generates a response from these elements.
- Recovery: searching for relevant documents or information.
- Increase : adding this information to the context provided to the model.
- Generation : production of a response based on the query and the retrieved elements.
What is RIG in AI?
RIG, or Retrieval Interleaved Generation Interleaved generation with retrieval (IGR), was popularized by Google in September 2024 with DataGemma, models designed to leverage the public statistical database Data Commons. The principle: instead of searching once before responding, the model is trained to identify, as it writes, passages that request verifiable data (a rate, a population, an amount). It then queries the source, retrieves the value, and inserts it into its response.
The term is sometimes used more broadly to refer to any approach where searching and generation alternate repeatedly during the same response.
RIG vs RAG: the main differences
| Criterion | RAG | RIG |
|---|---|---|
| Recovery time | Before the generation | During generation, throughout the text |
| What is recovered | Document excerpts | Often specific facts or figures |
| Interaction with sources | Context retrieved and then exploited | Multiple targeted requests in a single response |
| Complexity | Simpler architecture, very common | A trained or orchestrated model to formulate its queries |
| Typical uses | Questions and answers, document research, internal assistants | Responses rich in statistics, public data, or figures |
When to use RAG?
The RAG (Resource-Based Approach) is particularly suitable when the objective is to provide answers based on an identified corpus: document database, internal procedures, business knowledge, or reference documents. It allows for context enrichment without retraining the model each time the corpus is updated. This is the mechanism behind most assistants that are "connected" to a company's documents.
When to use the RIG?
An interleaved approach becomes useful when the answer contains many verifiable facts scattered throughout the text, when the data changes frequently, or when the main risk is numerical hallucination. Each value is then linked to a specific query, which facilitates verification.
What has changed since 2024: Agentic RAG, connectors and MCP
By 2026, the line between RAG and RIG has largely blurred in everyday tools. Assistants and agents no longer simply perform a single search before responding: they decide for themselves when to search, reformulate their queries, open new sources based on what they find, and then write their responses. This is often referred to as Agentic RAG, or deep research in consumer assistants.
On the business side, this research is increasingly being conducted through connectors to existing tools (email, SharePoint, Drive, CRM), often standardized by the Model Context Protocol (MCP)The principle of RAG remains the same, but the source is no longer necessarily a vector database built for the occasion: it is the business tool itself, queried on demand.
Examples of RIG and RAG in business
- Documentation assistant: Retrieving a procedure and then answering a specific question is a natural case for RAG.
- Economic report: An answer that cites rates, volumes and trends benefits from verifying each figure at the time it is written, in the spirit of the RIG.
- Multi-source analysis: successively searching for several pieces of information to construct an analysis falls under the category of agentic RAG.
- Professional conversation: as needs evolve through exchanges, successive searches enrich the context.
RIG or RAG: which to choose?
The choice depends less on a conflict between the two terms than on the type of information to be validated. For clearly defined documentation needs, a RAG architecture provides an effective starting point. For responses rich in numerical data, interleaved retrieval reduces the risk of fabricated figures. In both cases, quality depends primarily on the source: an up-to-date, structured database with controlled access rights.
To delve deeper into this topic, also consult our article How does RAG work in generative AI?.
To learn more: discover the Model Context Protocol (MCP) et the predictive capabilities of a set of LLMs.