What Is RAG? Teach AI to Work with Your Company Documents

In brief: RAG (Retrieval-Augmented Generation) means an AI model finds relevant passages in your own company documents before answering and uses them to write its response. The model is not retrained; information is retrieved from an external source at query time. This makes answers current, source-citing, and specific to your company. It reduces—but does not eliminate—the risk of hallucinations. Enterprise RAG success depends less on the model than on document quality, retrieval quality, access controls, and evaluation.

Last updated: September 2026

What is RAG, and why is it needed?

Large language models (LLMs) are trained on general information. They do not know your product catalog, internal policies, contracts, or technical documents, and they cannot know about developments after their training cutoff. This creates two problems: missing information and fabricated answers. RAG helps reduce both by supplying relevant documents as context before the model responds. The approach became widely known through the 2020 paper “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”, which combines information retrieval with generation.

RAG or fine-tuning?

• RAG: Best for information that changes often, needs citations, or lives in documents—policies, product documentation, contracts, and FAQs. When a document changes, you update the index, not the model.
• Fine-tuning: Useful for adjusting a model’s style, format, or behavior; it is not a reliable way to teach it factual knowledge.
• Most enterprise use cases start with RAG and combine the two only if needed.

RAG architecture, step by step

1. Collecting and processing documents

Gather PDFs, Word and Excel files, emails, wikis, ticket histories, and database records. PDFs may need OCR for tables, headings, and scanned pages; poorly parsed documents can mislead even the best model. Store source details as metadata alongside the text, including file, page, section, date, version, and access permissions.

2. Chunking

Split documents into meaningful passages. Chunk size, boundaries, and overlap directly affect retrieval quality: very small chunks lose context, while very large ones bring in irrelevant content. Splitting along headings and sections is often better than using a fixed character count.

3. Embeddings and indexing

Convert each passage into a numerical vector that represents its meaning and store it in a vector database, such as PostgreSQL with pgvector or a dedicated vector service. Test the embedding model’s Turkish performance on your own content.

4. Retrieval: hybrid search and reranking

Convert the user’s question into a vector and find the closest passages. Vector search alone can be weak for questions that need an exact match, such as a product code or clause number. That is why systems often use hybrid search—vector search plus keyword search such as BM25—followed by reranking.

Anthropic’s Contextual Retrieval method adds a short explanation of each passage’s place in the document before embedding it. In the reported results, contextual embeddings reduced retrieval failures by 35%, contextual BM25 by 49%, and reranking by 67% (from 5.7% to 1.9%). These results are not a guarantee for your data, but they show how much the search layer affects answer quality.

5. Generation: answers with sources

Give the retrieved passages to the model together with the user’s question. The model should answer only from these sources, cite the source document, page, or section, and say “I couldn’t find information about this in the documents” when the sources do not contain an answer. Define this behavior explicitly in the system instructions and test it.

6. Evaluation and monitoring

RAG quality cannot improve unless you measure it. See below.

Critical considerations for enterprise RAG

Access control by document

The most commonly missed issue is that not every user can access every document. Apply permissions during retrieval for HR documents, contracts, and financial reports so unauthorized documents are never sent to the model. Otherwise, the system may expose information to someone who is not allowed to see it.

Security: OWASP LLM Top 10

OWASP’s 2025 Top 10 for LLM applications applies directly to RAG systems:

• Prompt injection: Instructions hidden in a user prompt or indexed document can manipulate the model. Treat document content as “data,” not “instructions.”
• Sensitive information disclosure: Sensitive information may leak into answers.
• Vector and embedding weaknesses: The vector store may have access-control or data-isolation gaps.
• Excessive agency: A model may have too much permission to act, such as deleting data or sending email. Apply least privilege.
• Misinformation: The answer may be wrong or fabricated.
• Unbounded consumption: Uncontrolled usage can drive up costs; set rate limits and budget alerts.

KVKK and data residency

If documents contain personal data, privacy notices, data minimization, and retention periods apply. Know which model provider receives data and in which country it is processed. The Turkish data protection authority’s generative AI guide emphasizes that output is not always correct and that human oversight matters. Mask sensitive fields or exclude documents when needed.

Freshness and version management

When a document changes, refresh the index automatically, archive old versions, and show the document date in answers. A bot that answers from an obsolete policy can be more harmful than one that does not answer at all.

Measuring success

Prepare a “golden question set” of 50–200 real user questions with expected answers or sources, and track:

• Retrieval success: Is the correct source among the first N results?
• Faithfulness: Does the answer match the retrieved sources without fabrication?
• Answer relevance: Does it actually answer the user’s question?
• Citation accuracy.
• “I don’t know” behavior: Does the system abstain instead of making something up when there is no source?
• User feedback (thumbs up/down) and human handoff rate.

Run this set again after every change to chunking, model, or prompt.

Common use cases

• Customer support assistant: Source-grounded answers from product docs and FAQs, with handoff for unanswered questions (AI Customer Support Assistant).
• Internal knowledge assistant: HR policies, procedures, and onboarding.
• Contract and document analysis: Find, compare, and summarize clauses.
• Technical document search: For engineering and field teams.
• Chatbot knowledge base: For web and WhatsApp bots (chatbot guide, WhatsApp guide).

When is RAG not enough?

• For structured data queries (“How many orders this month?” or “How much stock is left?”), database queries or tool calls are more accurate.
• For work requiring complex calculations or a rules engine, use code instead of model predictions.
• If you have a disorganized pile of outdated documents, fix content management first: “garbage in, garbage out.”

What Aktaş Digital provides

Through our AI Document Analysis and RAG Systems service, we build enterprise RAG solutions with PDF/Word parsing, vector and hybrid search, source-citing answers, and access controls. We first define your documents and use case together and make quality measurement part of the system. Share your needs through our quote form.

Frequently asked questions

Does RAG completely prevent hallucinations?

No, but it reduces them. Requiring answers to be grounded in sources, citing those sources, and testing “I don’t know” behavior lowers the risk. Human oversight is still needed in critical areas.

Which document types work for RAG?

PDFs, Word files, presentations, wiki pages, FAQs, ticket histories, and product catalogs can all work. Results are better with high-quality, current, well-structured documents.

Is my data sent to an AI provider?

It depends on the architecture. If you use a model API, the query and retrieved passages are sent to the provider and must be considered under privacy and contractual requirements. For stricter privacy needs, consider a model running on your own infrastructure.

How long does RAG setup take?

A pilot with a limited document set and one use case can work within a few weeks. Document cleanup, permissions, evaluation, and integrations determine the timeline.

Does RAG work with Turkish documents?

Yes, but you should test the Turkish performance of the embedding model and search layer against real documents.

Conclusion

RAG is one of the most practical ways to connect AI to your company’s knowledge. With sound document processing, hybrid search, access controls, security, and continuous evaluation, it can become a reliable knowledge assistant for both employees and customers.