Introduction to RAG: How to Teach AI from Your Internal Documents

Introduction: Why "Teaching" AI Is Necessary

Large language models (LLMs) like ChatGPT and Claude are trained on enormous amounts of internet text and can answer general knowledge questions with impressive depth. But they know nothing about company-specific information — your product specifications, internal business processes, historical customer interaction records.

"We want to teach AI our internal information" is a request TIMEWELL receives constantly. The key technology here is RAG (Retrieval-Augmented Generation). This article explains the concept and construction process of RAG in a way that's accessible to non-technical readers.

What Is RAG?

How It Works

RAG is a technique that incorporates "retrieval" into the LLM answer-generation process. When a user asks a question, the system first searches internal documents for relevant information, then passes those search results to the LLM to generate an answer.

For example: the question "What is the warranty period for Product A?" arrives. The RAG system searches internal product specifications for information about Product A. Finding "Product A has a 2-year warranty," it passes this to the LLM, which generates the answer "Product A has a 2-year warranty."

How RAG Differs from Fine-Tuning

Another method for "teaching" internal information to an LLM is fine-tuning — re-training the LLM's weights on internal data.

But fine-tuning has drawbacks. It requires large amounts of training data and compute. When information changes after training, retraining is required. And it's difficult to show sources for learned information.

RAG addresses these issues. Information is held in an external database, making updates straightforward. Answers can be accompanied by source citations. For enterprise use, RAG has become the more popular approach.

Building a RAG System: Step by Step

Step 1: Collect and Organize Documents

The first step in RAG construction is collecting and organizing target documents — deciding which internal documents you want AI to reference and gathering them.

Product manuals, internal regulations, FAQs, past inquiry records, technical documents — select what's appropriate for your use case. Critically: exclude outdated and inaccurate information. "Garbage in, garbage out" applies directly to RAG.

Step 2: Chunking (Splitting)

Long documents need to be split into units that are manageable for search. This is called "chunking." Common chunk sizes are 500 to 1,000 characters.

Chunking approaches vary: splitting by character count, by paragraphs or headings, by semantic units. The optimal approach depends on the nature of the documents.

Step 3: Vectorization (Embedding)

Each chunk is converted into a vector — a sequence of numbers. Think of a vector as numerically representing the "meaning" of the text. Text that is semantically similar sits close together in vector space.

Vectorization uses dedicated embedding models. OpenAI's text-embedding-3 and Cohere's embed-v3 are representative models.

Step 4: Store in a Vector Database

Vectorized chunks are stored in a vector database — a database optimized for similarity search over vectors. Pinecone, Weaviate, Chroma, and Milvus are representative options.

Step 5: Search and Answer Generation

When a user submits a question, the following process runs:

The question is vectorized
The vector database retrieves chunks most similar to the question vector
The retrieved chunks (typically top 5–10) are passed to the LLM along with the question
The LLM generates an answer based on the provided information

ZEROCK's RAG Functionality

ZEROCK simplifies the RAG construction process described above. Users upload documents — chunking, vectorization, and storage happen automatically. No technical knowledge is required to build an AI-powered internal search capability.

ZEROCK also implements GraphRAG technology — which, in addition to conventional vector search, explicitly handles the "connections" between pieces of information. This improves the system's ability to handle complex questions.

Key Considerations in RAG Construction

Data Quality Management

RAG accuracy depends heavily on source data quality. Incorrect information, outdated information, and ambiguous wording all degrade AI answer quality. Regular data review and updates are essential.

Appropriate Chunk Size

Chunks that are too small lose context and degrade search accuracy. Chunks that are too large include extraneous information. Adjusting to an appropriate size based on document characteristics is necessary.

Hallucination Mitigation

LLMs can "make up" content not present in the provided information (hallucination). RAG reduces but cannot completely eliminate this. Displaying the source documents used to generate an answer — so users can verify the basis — is important.

Conclusion: Activating Internal Knowledge with RAG

RAG is a powerful technique for extending LLM capability into internal information. Properly built, vast internal document collections become accessible as naturally as asking a knowledgeable colleague.

ZEROCK abstracts away the complexity of RAG construction, making it easy for anyone to build an AI-powered internal search system. If you're interested in deploying RAG, we'd encourage you to try the 14-day free trial.

The next article compares NotePM and ZEROCK — features and selection criteria.