RAG

What is RAG?

Retrieval Augmented Generation (RAG) refers to an architecture in which a large language model (LLM) does not rely solely on its trained knowledge, but also searches a defined document repository with each query. The retrieved text fragments are passed to the model as context before it formulates a response. This allows the system to address current, company-specific, or confidential information that did not yet exist at the time of model training or was not included in training for data protection reasons.

Technically, RAG consists of two main components: a retrieval module that extracts relevant passages from a vector database or a conventional search index, and the generative model that evaluates the retrieved content and synthesizes it into a coherent response. The overall quality of the system depends heavily on both parts – a powerful language model is of little use if the retrieval delivers irrelevant or outdated documents.

Application in intralogistics and procurement

In logistics and procurement environments, RAG opens up concrete use cases:

• Evaluation of technical documentation such as warehouse management or conveyor technology manuals, without employees having to manually search through lengthy documents
• Intelligent search across vendor and item master data, contracts, or tender documents
• Context-aware answering of inventory planning questions based on current stock or movement data, provided this data has been transferred into a suitable database
• Support for compliance checks through automated searching of policies and regulatory frameworks

It is important to note that RAG is not a cure-all. The quality of responses stands or falls with the quality and currency of the underlying documents. Unstructured or erroneous data will lead to unreliable results even with RAG.

What to look for when evaluating solutions

Anyone looking to implement RAG in a logistics environment should examine the following points: Which data sources are to be connected, and how is their regular update ensured? How is the relevance ranking of retrieved documents configured and monitored? Are there mechanisms to detect and prevent hallucinations – i.e., fabricated but plausible-sounding responses? And how is it ensured that sensitive company data is not transmitted uncontrolled to external AI services? These questions must be answered on a vendor-by-vendor basis and should be part of every evaluation.