LLM with RAG: The bridge between corporate data and AI
Retrieval-Augmented Generation (RAG) combines the power of large language models (LLMs) with up-to-date, company-specific knowledge without compromising sensitive data. In this article, we show the advantages RAG offers companies and how the approach can be integrated into your own infrastructure step by step.
AI has long been more than just a trend in everyday business life. Generative language models (known as large language models, or LLM for short) support writing texts, responding to customer inquiries more quickly, and efficiently searching for and processing knowledge.
But many companies face the question: How can the immense efficiency potential of AI be exploited without revealing sensitive data or becoming dependent on cloud providers?
The answer: with locally hosted LLMs in conjunction with retrieval-augmented generation (RAG).
This approach combines the linguistic intelligence of modern AI models with direct access to up-to-date, company-specific knowledge. The advantage is that confidential knowledge and sensitive data remain where they belong: within the company. Sounds like a good combination of performance and control? Exactly.
Explaining the RAG approach
Retrieval-Augmented Generation (RAG) expands generative language models with current and company-specific knowledge.
- An LLM remains limited to its original training data.
- RAG adds access to internal information in real time.
A RAG application filters out the most relevant information from large datasets in real time. The key to this is vector search in a vector database.
To do this, all texts are processed in advance by an embedding model. This model converts each text into a high-dimensional vector – a series of numbers that represents the semantic meaning of the content. Texts with similar meanings are therefore stored close together in the vector database.
When a query is made, it is also translated into a vector. The system then mathematically compares the query vectors with the stored document vectors. The most semantically similar content is identified and passed on to the LLM.
In short:
- Embedding: Texts are converted into vectors that represent their meaning.
- Storage: These vectors are stored in a vector database, optionally with metadata (source, date, page).
- Retrieval: A query is also translated into a vector and compared with the stored vectors.
- Response (generation): The LLM creates a response based on the matching text passages.
Result: Current facts from your sources instead of hallucinations. Sounds good, right?
Advantages of RAG
RAG not only provides more precise answers, but also changes the way companies use their data. In addition to improved result quality, there are other key advantages:
The following steps have proven effective in practice:
- Data protection & sovereignty
Since data in the RAG approach is processed locally or in a controlled infrastructure, sensitive information does not leave the company. Trade secrets, customer data, and confidential processes remain protected and under your own control. Do we prefer AI without data leakage? Of course! - Intelligent search & semantics
RAG goes beyond simple keyword hits and enables semantic search. This means that queries are interpreted according to their meaning. If you search for "vacation policy," you will also get documents with "absence guidelines" or "vacation approvals." Synonyms, abbreviations, and different formulations are thus reliably captured.
The result: less searching, more finding. - Cost control & efficiency
Another advantage of RAG is that new information does not require expensive model fine-tuning. Instead of repeatedly retraining the LLM, it is sufficient to update the vector database. Retrieval dynamically fetches the relevant information at runtime, which is significantly more efficient. The result: lower running costs, flexible scaling, technical agility.
Data preparation for RAG: How a vector database is created
In order for an LLM with RAG to deliver reliable results, the underlying vector database must be regularly maintained and kept up to date. Data preparation is therefore not a one-time step, but an ongoing process. The advantage of this is that new or changed content can be imported at any time without having to retrain the language model itself. That sounds like a lot of work, yes. But the effort involved can be planned.
The following steps have proven effective in practice:
- Identify sources
Select relevant documents – such as PDFs, websites, manuals, support wikis, or internal reports. - Preprocessing
Raw documents are converted into a suitable text format. This may involve converting images into text, extracting tables, and removing unnecessary elements such as headers or page numbers. - Chunking
The content is broken down into smaller sections ("chunks"). The size of these chunks is crucial for the quality of subsequent responses and should be tested depending on the document type. - Embedding
An embedding model is used to convert the text sections into vectors. These vectors represent the meaning of the content and enable mathematical comparison with user queries at a later stage. - Storage in the vector database
The embeddings are stored in a vector database that is specially optimized for the efficient management and searching of large vector quantities. Additional metadata (such as source, date, or page number) can be used to filter results and display them transparently.
Conclusion and outlook
The combination of LLM and RAG gives companies the opportunity to combine current information with internal knowledge. However, clean data preparation and continuous maintenance are crucial for this approach. This enables LLMs with RAG to provide accurate and context-sensitive answers.
But the introduction of RAG also brings challenges: How can result quality be measured and evaluated? What needs to be considered during implementation? And what does a concrete use case look like in practice?
A good next step: our free white paper with evaluation criteria, implementation checklist, and a real-world use case that explains how RAG works step by step.
