What is RAG? A practical guide to retrieval-augmented generation for enterprise knowledge

RAG (Retrieval-Augmented Generation) is a technique that makes a large language model first retrieve relevant information from a knowledge base you specify, then generate answers based on what it finds. The model checks your documents before responding instead of making things up from memory.

A life insurance company built an internal Q&A tool using an off-the-shelf language model. When asked "How many days is the waiting period on this policy?" it provided a confident answer that was incorrect, it quoted rules from a competitor's product. The model itself wasn't defective; it had never read this company's actual policy documents and could only guess based on general insurance knowledge found online. This illustrated why RAG addresses a genuine need in enterprise knowledge scenarios.

What is RAG? A simple analogy

Consider a large language model as an intern who writes well and has a decent memory. She's read many books and has good language instincts but has never seen your company's contracts, SOPs, product specs, or yesterday's meeting notes. Asked about a company policy, she'll answer from memory. The response sounds fluent, but the details might be incorrect. This is called hallucination.

RAG provides a study guide before generating a response. The system takes a question, retrieves the most relevant document passages from your knowledge base, and presents both the question and passages to the model with one requirement: answer only based on this material. The intern shifts from working from memory to working from the textbook. The answer's accuracy depends entirely on whether the provided material is correct.

The process has three components. First, split documents into chunks and convert them to embeddings stored in a database, this is indexing. When someone asks a question, convert it to an embedding and retrieve the semantically closest chunks, this is retrieval. Feed those chunks as context to the model to generate an answer, this is generation. The difficulty concentrates in the first two steps.

Why enterprise knowledge scenarios require RAG

Pure language models face significant constraints in enterprise settings. RAG addresses each one.

Traceability comes first. When asked for the source of a number, RAG can identify the specific page of the relevant report. A pure model cannot identify its sources. In finance or healthcare, where errors have serious consequences, the ability to cite sources is essential before deployment.

Updates are the second constraint. Product specifications change regularly. Retraining a model each time requires significant time and resources. With RAG, new documents enter the knowledge base and take effect immediately.

Access control is the third limitation. Marketing should not see HR compensation data. The retrieval layer can filter documents by role, the model only sees content the person is authorized to access.

The hard part: getting retrieval right in production

RAG demos often appear deceptively successful. A demonstration using ten clean documents and ten prepared questions typically performs well. Production reveals actual difficulties. Chunks that are too small lose context around individual sentences. Chunks that are too large bury signal in noise, causing the model to focus on incorrect details. Tables, scanned PDFs, synonym variations, and internal jargon all create retrieval problems. When retrieval fails, model sophistication becomes irrelevant; the system generates confident answers based on incorrect information.

Success depends on fundamentals that are not glamorous: how documents are chunked, which embedding model is selected, whether keyword hybrid search is applied, whether sources are cited, and whether accuracy is measured continuously with a test suite rather than subjective assessment.

At Tenten, we don't write code in the first week when building RAG knowledge systems. Initial focus is on the customer's documents and questions, establishing what constitutes a correct answer for each query type, then tuning retrieval accordingly. Beautiful demos don't equal deployment. The system is live when employees use it daily and trust the results.