LLMs & AI Agents · RAG
Retrieval Augmented Generation (RAG): 5 Use Cases for Companies
A language model does not know your products, policies or contracts. Retrieval augmented generation connects it to your own documents, so answers rest on your knowledge and name the source. These five use cases show where that pays off for companies.
Julia Rose
Key takeaways
- RAG finds the relevant passages in your documents for a question and has a language model write an answer from them, with the source.
- Typical uses are customer and citizen inquiries, internal knowledge search, questionnaires, onboarding and digital assistants.
- At apoQlar, RAG cut the time needed for security questionnaires by 75%.
- How well a RAG system answers depends mostly on the quality, currency and access rights of the data.
What retrieval augmented generation is
Large language models write fluently, but they only know their training data. Internal documents, current prices or the latest product version are not part of it. Ask anyway, and in the worst case they invent a plausible answer.
Retrieval augmented generation (RAG) solves this in two steps:
- Retrieval: for a question, the system finds the relevant passages in your documents, usually through a vector database that searches by meaning.
- Generation: the language model receives these passages as context and writes the answer from them, with a reference to the source.
The knowledge stays in your documents, and a change there shows up in the answers immediately, with no model retraining.
5 use cases for RAG in companies
1. Answering customer and citizen inquiries
People ask in their own words, while the answers sit in technical documentation, manuals or regulations. A RAG assistant bridges the two around the clock and names the passage the answer came from.
In practice: technical inspection authority
A Polish technical inspection authority receives thousands of inquiries about permits, equipment safety and certification. Its previous chatbot made people pick a category first. We designed a multi-agent architecture with RAG that routes each question automatically across 11 specialised knowledge areas and matches everyday language to technical documentation.
2. Finding internal knowledge fast
Product knowledge, process descriptions and reference material often live in hundreds of documents. Whoever has to answer a question searches by hand, and the quality of the answer depends on how much time they have.
In practice: apoQlar
The MedTech company’s support and operations teams searched hundreds of product documents by hand. A RAG assistant with a vector database now answers questions in seconds and names the document each answer came from. The team verifies instead of searching and can pass the answer straight to customers.
3. Completing questionnaires and tenders
Security and compliance questionnaires, supplier audits and tenders often ask the same questions in new wording. RAG drafts the answers from policies and technical documentation, and experts review and approve them.
In practice: apoQlar
Before every hospital rollout, apoQlar had to complete extensive security questionnaires, each taking about a month. With a RAG assistant, processing time fell by 75%, and onboarding new clients went from six weeks to two.
4. Onboarding new employees
In their first weeks, new colleagues have many questions about policies, processes, tools and responsibilities. A RAG assistant answers them from handbooks, training material and the intranet at any hour, with a link to the original document. Experienced staff are interrupted less often, and the answers stay consistent.
5. Digital assistants and AI avatars
At trade fairs, at reception or on the website, AI avatars talk to visitors. Through RAG they answer from your documents or the event database, by voice and in several languages.
In practice: XPOMET Medicinale
At XPOMET Medicinale 2024 in Leipzig, the AI avatar Paula, built together with Johannesstift Diakonie, answered visitor questions about navigation and exhibitors in real time, in German and English and in a noisy conference hall.
What matters with RAG
A RAG system is only as good as the data it answers from. Outdated or contradictory documents lead to wrong answers, especially in fields such as medicine or law. Four points decide the quality:
- Data quality: current, reviewed documents and a clear rule on which version applies.
- Preparation: documents are split into meaningful sections, and tables and scans are made readable.
- Access rights: the system only shows each person what they are allowed to see in the original.
- Evaluation in operation: answers are measured continuously against real questions so errors surface early.
How companies put this into practice is covered in our article How enterprises build reliable RAG systems. An overview of our services is on the RAG system development page.
Putting the knowledge in your documents to work?
We look at which documents and questions suit RAG and how a first assistant fits into your systems.
Request a process analysisFrequently asked questions
A method in which a system finds the relevant passages in company documents for a question and a language model writes an answer from them, with the source.
For example customer and citizen inquiries, internal knowledge search, security questionnaires and tenders, onboarding new employees, and digital assistants or AI avatars.
No. The knowledge stays in the documents. When they are updated, the changes show up in the answers immediately.
Mostly on current and reviewed data, good document preparation, proper access rights and continuous evaluation of the answers in operation.
Julia has been part of theBlue.ai since 2019 and has accompanied the development of AI applications in the enterprise environment since the company’s early days. In her role as Marketing Lead, she works closely with the engineering and consulting teams and makes complex technical topics understandable and accessible for decision-makers.
In her articles, she writes about practical experience from enterprise AI projects, as well as the challenges and opportunities of using AI in companies.