What is Retrieval-Augmented Generation?
Retrieval-Augmented Generation (RAG) describes an approach to artificial intelligence (AI) where responses are generated using information retrieved from external sources at the moment a question is asked. Rather than relying only on what a language model learned during training, RAG allows the system to draw from known records that reflect current conditions,
governed content and domain-specific knowledge.
In industrial facilities, RAG is used to describe AI behavior that is grounded in real information. The defining characteristic is not how fluent the response sounds, but whether it reflects the material that organizations already rely on to run, maintain and govern physical assets.
How RAG Alters AI
RAG changes the role of the language model from a standalone generator to a synthesizer of retrieved content. The system no longer answers a question in isolation. It first identifies information that is relevant to the question, then uses that material as context for generating a response.
This shift has important implications. Responses become shaped by what the system retrieves rather than by probability alone. The AI reflects what it has been shown, even though the output is still written in natural language. This behavior makes RAG fundamentally different from AI systems that respond without access to source material.
What Information Is Used in RAG Systems
RAG systems are designed to retrieve information from
defined and governed sources. In industrial contexts, these sources describe assets, work and operating conditions rather than abstract concepts.
Typical sources include:
- Engineering drawings, specifications and models that define how assets are designed.
- Maintenance and inspection records that capture how assets behave over time.
- Procedures, standards and technical manuals that govern execution.
After retrieval, this information becomes the reference point for the generated response. The AI is not inventing new facts. It is interpreting and summarizing what already exists within those sources.
Why RAG Depends on Information Context
Retrieval alone does not make AI useful. The usefulness of RAG depends on how well information is structured, connected and contextualized. Documents that lack asset references or records that are disconnected from asset lifecycle context limit what retrieval can surface.
When information is
well organized and linked to assets, systems and locations, RAG can reflect that structure in its responses. When information is fragmented, RAG mirrors that fragmentation. In this way, RAG amplifies the state of the underlying information environment rather than correcting it.
