What Is RAG? AI That Works with Your Company Data

01What Is RAG and Why Is It Needed?

Large language models are strong in general knowledge, but they do not know your company's price list, your internal procedures or the return policy that was updated last month. That information is not in the training data, and even if it were, it would be out of date. The shortest answer to what is RAG is this: a search layer that makes the model read the right document before it answers the question.

The three words in its name summarize the process. Retrieval means finding the relevant pieces of text, augmented means adding those pieces to the question, and generation means the model writing the answer with that context. The model behaves like a student taking an open-book exam.

This approach stands out especially in enterprise AI projects. In areas such as customer service, sales support, HR handbooks and technical documentation, it lets the model safely use knowledge it does not have but you do.

02How Does RAG Work?

A RAG system consists of two separate flows: the indexing flow, in which documents are prepared in advance, and the query flow, which runs with every question. Indexing is done once (and again as documents change); the query flow completes within seconds for every user message.

The indexing flow

PDFs, Word files, web pages or database records are converted to text and split into meaningful chunks. Each chunk is turned into a numeric vector by an embedding model and saved to a vector database. Vectors represent the meaning of the text; sentences with similar meanings produce vectors that are close to each other.

The query flow

1. The user's question is turned into a vector with the same embedding model.
2. The vector database is searched for the chunks closest to the question (usually the top 3 to 10 results).
3. The chunks found are sent to the language model together with the question and the instructions.
4. The model writes the answer based only on this context and names the source.

03Vector Database and Embeddings

Classic search looks for word matches; someone searching “refund period” can miss a paragraph that says “right of return.” Semantic search, on the other hand, measures closeness between vectors, so it recognizes the two phrases as the same topic. Much of RAG's power comes from this step.

Special-purpose vector databases can be used to store vectors, and an existing relational database can also take on the job with extensions such as pgvector for PostgreSQL. Which one fits is decided by data volume, update frequency and the technology your team knows. We covered the choice of data model in detail in our article on the difference between SQL and NoSQL.

In practice the best results often come from hybrid search: the results of semantic search and keyword search are combined. For phrases that must match exactly, such as a product code or contract number, keyword search is still more accurate.

import numpy as np

def kosinus(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# parca_vektorleri: her belge parcasinin embedding vektoru
# soru_vektoru: kullanici sorusunun embedding vektoru
skorlar = [kosinus(soru_vektoru, v) for v in parca_vektorleri]
en_iyi = np.argsort(skorlar)[::-1][:5]
print(en_iyi)

04RAG or Fine-Tuning?

The two ways of teaching company data to a model are often confused. Fine-tuning means retraining the model's weights with your own examples; it is good for teaching style, format and specific task patterns. RAG, by contrast, keeps the knowledge outside the model and brings it in with every question.

If information changes often, if citing sources matters and if access permissions differ by user, RAG is almost always the more accurate starting point. The two methods can also be used together.

05Use Cases and Examples

Chatting with documents is the best-known example: instead of reading a procedure hundreds of pages long, employees ask questions and the system answers by quoting the relevant clause. The same logic works on the customer side too.

• Customer support assistant: answers questions about shipping, returns and warranty terms using the current policies on the site.
• Hotels and tourism: answers guest questions from room features, property rules and tour programs; for reservation data it is considered together with systems such as hotel software.
• E-commerce: compares products from descriptions and technical sheets, while live data such as stock and price is read directly from the system through e-commerce integration.
• Internal knowledge base: HR, accounting processes and technical setup notes are searched from a single interface.
• Marketing team: produces drafts based on the brand guide and past campaign reports; these drafts go through human approval in the digital marketing process.

06How to Set Up a RAG Project

Even though the technical components are ready-made, most of the effort in a successful project is on the data side. The steps below offer a roadmap that holds for most enterprise projects.

1. Narrow the scope: first pick a single use case, for example only customer support questions.
2. Clean the sources: weed out old, contradictory and draft documents; a wrong document does not produce a right answer.
3. Decide the chunking strategy: split by heading and paragraph boundaries, and leave a small overlap between chunks.
4. Design access permissions: add to each chunk, as metadata, who is allowed to see it.
5. Write the instruction: ask the model to answer only with the given context and to say so if it does not know.
6. Prepare a test set: measure regularly with 50 to 100 real user questions and the expected answers.
7. Set up the update flow: make sure the relevant chunks are re-indexed automatically when a document changes.

07Common Mistakes

RAG systems look impressive in the first demo; problems appear with real users. The most common mistake is poor chunking: a table is split in two, the heading and its content land in separate chunks, and the model answers with half the information.

The second mistake is looking only at the final answer without measuring retrieval quality. The source of a wrong answer is often not the model but the wrong chunk being retrieved. That is why search results should be evaluated separately.

The third is security. Instructions hidden inside documents can try to steer the model (prompt injection). Retrieved text should always be treated as data and must never override system instructions. If the model will connect to other systems, the permission principles in our article What is MCP apply here too.

Finally, RAG is not the whole flow. If you need to open a ticket, send an email or update a record after the answer, it has to be combined with workflow automation tools.

08Before Starting a RAG Project

Ask yourself three questions: Which questions take up employees' time the most today? Are the answers current and written in one place? What does a wrong answer cost? The answers to these questions determine the scope of the project and the point at which human approval is needed.

If you will work with documents containing personal data, the conditions for processing and storing data must be clear from the start. Where the model service you use processes data, and whether it stores it, should be checked in the contract; for a legal assessment, check current regulations and your adviser.

At Aurora Yazılım, when we build industry software solutions, we put where the data sits and who can access it at the center of every project. If you would like to discuss your project, you can reach us through our contact page.