What Is RAG?

RAG (Retrieval-Augmented Generation) is a technology that retrieves documents held by your organization and uses them in AI responses. The biggest advantage is that you can connect accumulated internal knowledge to AI workflows without retraining the AI model itself.

While a typical conversation relies only on the AI model's learned knowledge, RAG works by first finding relevant documents (retrieval) when a question comes in, then generating an answer based on that content (generation). This allows you to present source references alongside answers, and when documents change, you only need to update the search data without model retraining.

Why RAG for Internal Knowledge Search

Companies accumulate manuals, work guidelines, technical documents, reports, and meeting notes. The problem is that this knowledge is scattered across files and systems, making it hard for the right person to find it at the right moment.

Deploying a RAG-based knowledge search AI brings the following changes:

  • Natural language questions: Instead of searching documents one by one, just ask "What was the server outage response procedure last quarter?"
  • Answers with sources: Shows the document location that served as the basis, increasing trust
  • Data control: Sensitive documents can be processed in the internal environment without external transmission
  • Easy updates: When documents are modified, simply regenerate the search data (embeddings) to reflect changes

RAG System Components

1. Document Collection and Preprocessing

Collect documents in various formats: PDF, HWP, Word, text, etc. It's important to extract while preserving document structure (headings, table of contents, tables) as much as possible.

2. Chunking

Split long documents into pieces of a size suitable for AI processing. Search quality varies significantly depending on the chunking method. Strategies matching document characteristics are needed: paragraph-based, fixed-length, or semantic chunking.

3. Embedding and Vector Storage

Convert each piece into a numerical vector (embedding) and store it in a vector database. When questions are vectorized the same way, semantically similar document pieces can be found quickly.

4. Retrieval and Reranking

Retrieve candidate documents similar to the question, then rerank them by relevance. The quality of this step determines the quality of the final answer.

5. Answer Generation

Pass the ranked document pieces as context to the AI model to generate an answer. Prompt design like "Answer only based on the provided materials, and state that information is unavailable if not in the materials" is the key to reducing hallucination.

Considerations for Deployment

Search quality starts with data quality. If documents are not organized, AI cannot give accurate answers. Before deployment, assess the current state of documents and narrow the scope starting from frequently asked topics.

Model selection depends on your environment. If you have sensitive data, configure with local models. If performance is priority, consider a hybrid structure combining external APIs and local models.

You need measurement metrics. Rather than a single metric like "accuracy rate," collecting how frequently it's used in actual work and user feedback on answers becomes the foundation for continuous improvement.

Frequently Asked Questions

Q. What's the difference between RAG and model training (fine-tuning)? RAG retrieves documents and uses them in answers, while fine-tuning trains the model itself. For internal knowledge search where knowledge updates frequently and source citation is needed, RAG is generally more suitable.

Q. What document formats are supported? Most formats are supported: PDF, HWP, Word, text, Markdown, etc. Documents with many tables and images require additional preprocessing strategies.

Q. How long does deployment take? It varies by document volume and environment, but a pilot starting with core documents can be completed in a matter of weeks.

If you're interested in building a RAG system that connects your internal documents with AI, check the RAG service page for details or contact us to start a consultation.