From documents to answers: RAG and generative AI over your archive, with verifiable citations
Davide Antonio Amodio · · 2 min read
From search to answer
A semantic search engine finds the right documents; but the person asking often does not want a list of files, they want the answer. The step beyond search is an assistant that reads the relevant documents and phrases an answer in natural language, stating where it came from. This is what RAG, Retrieval Augmented Generation, enables.
How RAG works, in short
RAG works in two stages. First the retrieval stage: the question is turned into a vector and compared against the document index, pulling out the most relevant passages. Then the generation stage: those passages are passed to a language model, which composes the answer based only on them. The model does not draw on a vague memory: it answers over concrete context, that of your documents.
Why citations are mandatory
The difference between a useful assistant and a dangerous one is citations. Every statement must be traceable to the document and passage it comes from, so the reader can verify it. Without citations, a model can produce plausible but wrong sentences; with citations, the user checks the source in one click and the organisation keeps the traceability that regulations require.
Privacy: the data stays in-house
Company documents contain sensitive data: contracts, invoices, personal information. A good system keeps the documents, the vector indexes and the processing within the company perimeter or with a GDPR-compliant European provider. The principle is simple: value is extracted from the data without letting it leave, and without handing it to third-party services outside the agreed boundaries.
Where to start
You do not need to redo everything. Start from a well-bounded set of documents and from real user questions, measure the quality of answers and citations, and expand gradually. The foundation is an archive that is already structured and searchable: on top of that, adding the generative answer layer is an increment, not a revolution.
FAQ
Can RAG make up information?
The risk exists if the system is poorly designed. By constraining the answer to the retrieved passages and always showing citations it drops sharply, because the user verifies the source and unsupported answers are spotted immediately.
What is the difference between semantic search and RAG?
Semantic search returns the most relevant documents; RAG adds one more step and phrases the answer by reading and citing those documents. Often semantic search is the retrieval engine that RAG relies on.
Do you need my data to train a model?
No. RAG uses the documents as context at question time, not to retrain the model. This matters for confidentiality: the data is not permanently absorbed into the weights of an external model.