Retrieval-Augmented Generation bridges the gap between in-house expertise and the ability of large language models such as ChatGPT to generate natural language without the need for constant training, revolutionizing document processing.
This connection can offer many advantages and opportunities for companies if it is used sensibly.
RAG is the abbreviation for Retrieval-Augmented Generation and stands for a software system which combines information retrieval with a LLM(Large Language Model).
The term information retrieval refers to the retrieval of information, usually from databases, or more precisely, computer-aided queries of complex content. Search engines such as Google use IR, as do digital libraries, for example.
RAG therefore combines two AI approaches, namely the retrieval of information or documents from a database and the generation of an answer based on the retrieved information.
RAG enables the system to access not only data sources such as the Internet or existing training data, but also other external data sources provided, such as internal company data like training courses, manuals, etc., during a query. The aim here is to feed the system with specific information and make it findable using IR.
In this way, a company can enable access to internal information using LLM without having to disclose sensitive data. The up-to-dateness of the answers is also guaranteed. Internal manuals, documentation or training courses can be made available and used in a. skalable manner. This also ensures lower hallucinations of the LLM.
DMS / ECM are used in many companies. The indexing of new incoming dokuments to make them retrievable is usually a time-consuming and sometimes manual process. Previously, the document was often full-text indexed in order to get out of the rigid metadata-based search. In searches this in turn often resulted in the problem of false positives (false matches of criteria), as the context was simply missing.
Today, RAG can recognize the content of documents, create an index and take the corresponding kontextof the query into account so that exactly the information that was searched for is delivered.
The document receipt – i.e. the processing and categorization of incoming documents such as e-mails, PDFs, letters or forms – is a classic application area for RAG, especially in combination with OCR, NLP and automatic classification.
Companies receive a large number of documents, often in an unstructured format. These need to be classified, understood and often processed automatically. RAG supports classification and contextual understanding, as soon as the document is received, e.g. whether it is a complaint or an invoice. The extraction of relevant information can also be greatly facilitated by RAG, as can the categorization and further processinge, such as automatic forwarding to the accounting department.
As described above, RAG also enables automatic indexing of documents and intelligent metadata recognition from context and retrieval , to make them easier to process or find again. Missing fields can also be automatically completed by the AI.
RAG not only revolutionizes the receipt of documents, but also the search for documents.
An example of a corporate use case could be a chatbot for queries to internal company document collections, where each newly added document is indexed by the AI and an LLM assists in interpreting the context of the query. In this way, RAG could serve as an employee assistent to facilitate access to internal guidelines or processes.
A RAG system can be particularly helpful in support For customer support, a chatbot could automatically answer queries by accessing documentation, manuals or CRM data. RAG can also answer questions in technical support or DevOps based on internal documentation, code snippets, log files or other knowledge sources.
In the compliance area, RAG can facilitate access to standards or guidelines from extensive documents (e.g. ISO).
In traditional document management or workflow systems, workflows are controlled via predefined metadata,such as document type (invoice, complaint), customer, order number and many more. RAG can trigger defined workflows based on recognized patterns, such as approval, forwarding or archiving.
One example of this is invoice processing. Without RAG, the document must be supplemented manually with metadata such as invoice number, payment date, etc. after it has been scanned by OCR. Traditionally, the document is then checked and completed by a clerk and the approval process is then started. With RAG, the type of document can be automatically recognized after the OCR scan, missing fields are added and the correct workflow is automatically triggered.
RAG brings contextual understanding and semantic flexibility to the otherwise often rigid, rule-based workflows of document processing. It enhances classic metadata extraction with intelligent context evaluation, increases the degree of automation and reduces effort – especially in heavily document-driven industries such as legal, HR, purchasing, finance or compliance.
RAG is a game changer for companies that want to use large amounts of structured or unstructured knowledge efficiently. It manages the balancing act between the performance of modern language models and the reality of day-to-day business: knowledge is often scattered – RAG brings it to the right place at the right time, which can lead to an increase in productivity, democratization of knowledge or even an increase in customer satisfaction, depending on the area of application.