Retrieval-Augmented Generation bridges the gap between a company's proprietary know-how and the ability of large language models such as ChatGPT to generate natural language without the need for constant training, revolutionizing document processing.
This link can offer companies many advantages and opportunities when used sensibly.
What does RAG mean?
RAG is the abbreviation for Retrieval-Augmented Generation and refers to a software system that combines information retrieval with an LLM (Large Language Model).
The term information retrieval refers to the retrieval of information, usually from databases; more specifically, it involves computer-assisted queries of complex content. Search engines such as Google use IR, as do digital libraries, for example.
RAG thus combines two AI approaches: retrieval—i.e., the retrieval of information or documents from a database—and generation—i.e., the generation of a response based on the retrieved information.
Advantages of RAG for companies
RAG enables the system to access not only data sources such as the Internet or existing training data when a query is made, but also other external data sources that are made available, such as internal company data such as training courses, manuals, etc. The aim here is to feed the system with specific information and make it findable using IR.
This allows a company to enable access to internal information using LLM without having to disclose sensitive data. The timeliness of the responses is also guaranteed. Internal manuals, documentation, or training courses can be made available and used in a scalable manner. This also ensures fewer hallucinations from the LLM.
Many companies use DMS/ECM. Keyword indexing of incoming new documents to make them retrievable is usually a time-consuming and partly manual process. Until now, documents were often full-text indexed in order to get away from rigid metadata-based searches. This, in turn, often resulted in the problem of false positives (incorrect matches of criteria) because the context was simply missing.
Today, RAG can recognize the content of documents, create an index, and take the relevant context of the query into account when searching, so that exactly the information that was sought is delivered.
Use cases of RAG for companies
1. document receive
Document intake—the processing and categorization of incoming documents such as emails, 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 must be classified, understood, and often processed automatically. RAG already supports document intake with classification and context understanding, i.e., whether it is a complaint or an invoice, for example. RAG can also greatly facilitate the extraction of relevant information, as well as categorization and further processing, such as automatic forwarding to accounting.
In addition, as described above, RAG enables automatic keyword tagging of documents as well as intelligent metadata recognition from context and retrieval to make them easier to process or retrieve. Missing fields can also be automatically completed by AI.
2. document search
RAG is revolutionizing not only document intake, but also document search.
An example of a use case in companies could be a chatbot for queries to internal document collections, whereby each newly added document is indexed by AI and an LLM assists in interpreting the context when a query is made. In this way, RAG could serve as an employee assistant 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. In technical support or DevOps, RAG can also answer questions based on internal documentation, code snippets, log files, or other knowledge sources.
In the area of compliance, RAG can facilitate access to standards or guidelines from extensive documents (e.g., ISO).
3. Automated document processing (workflows based on fixed metadata)
In traditional document management or workflow systems, workflows are controlled by predefined metadata, such as document type (invoice, complaint), customer, order number, and many more. RAG can trigger defined workflows such as approval, forwarding, or archiving based on recognized patterns.
One example of this is invoice processing. Without RAG, after being scanned by OCR, the document must be supplemented manually with metadata such as invoice number, payment terms, etc. Typically, an administrator then checks and supplements the document before starting the approval process. With RAG, the type of document can be automatically recognized after OCR scanning, missing fields are supplemented, 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 extends classic metadata extraction with intelligent context analysis, increases the degree of automation, and reduces effort—especially in highly document-driven industries such as legal, HR, purchasing, finance, and compliance.
Conclusion
RAG is a game changer for companies that want to efficiently utilize large amounts of structured or unstructured knowledge. It bridges the gap between the performance of modern language models and the reality of everyday business life: knowledge is often scattered – RAG brings it to the right place at the right time, which, depending on the area of application, can lead to increased productivity, democratization of knowledge, or even increased customer satisfaction.