Retrieval-Augmented Generation: Grounding Enterprise AI in Your Own Data

The problem RAG exists to solve

A large language model only knows what was in its training data, up to a cutoff date — it has never seen your internal wiki, your product catalog, or last week's support tickets. Fine-tuning a model on that data is possible, but it's expensive to keep current and it doesn't let the model cite where an answer came from. Retrieval-Augmented Generation (RAG) takes a different approach: instead of teaching the model your data, it hands the model the relevant pieces of your data at the moment it answers.

How it works, step by step

1. Embed: your documents (or table rows, or support tickets) are converted into vector embeddings — numerical representations that place semantically similar content close together in vector space.

2. Store: those embeddings are saved in a vector database (or a vector index inside your existing warehouse) alongside a reference back to the original document.

3. Retrieve: when a question comes in, it's embedded the same way, and the vector store returns the handful of documents most semantically similar to it.

4. Augment and generate: those retrieved documents are inserted into the model's prompt alongside the question, so the model answers using your actual content instead of relying on what it memorized during training.

A minimal example


query_embedding = embed(question)
top_docs = vector_store.similarity_search(query_embedding, k=4)

prompt = f'''
Answer using only the context below. If the answer isn't in the
context, say you don't know.

Context:
{format_docs(top_docs)}

Question: {question}
'''
response = llm.generate(prompt)

The instruction to only use the provided context is doing real work here — it's what keeps the model from filling gaps with plausible-sounding but ungrounded answers.

Where RAG fits into a real data platform

RAG isn't a replacement for your warehouse — it's a new consumer of the same governed data. The documents that get embedded still need the same access controls and classification discussed in our article on data governance for AI: if a document is restricted in the warehouse, it should never end up retrievable by a model serving a broader audience. Treating the vector store as just another governed data product, not a side project, is what keeps a RAG system trustworthy as it scales past the first demo.

Artificial Intelligence