Why combine research and generation
A language model knows what was learned during training, but not necessarily the procedures, contracts or standards specific to your company. The RAG first searches for relevant information in an authorized corpus, then provides it to the model to construct its answer.
This approach makes it possible to update knowledge without retraining the model and to link the answers to the passages used. It reduces the risk of unverifiable assertions, without eliminating it: evaluation and validation remain essential.
The steps of a RAG chain
Documents are collected, transformed into text, divided into coherent units then enriched with metadata. A numerical representation then makes it easier to find passages close to the question.
At query time, the system filters authorized sources, classifies the results and composes a context for the model. The response must retain the necessary references for verification.
- Content ingestion and extraction
- Slicing adapted to the document type
- Indexing and metadata
- Searching and reclassifying passages
- Generation with citations
- Measurement of the quality
What a private deployment changes
In a private RAG, documents, index, queries and responses remain in the environment defined by the organization. This control is important when the corpus contains commercial, legal, technical or personal information.
Confidentiality does not, however, rely solely on hosting. Access rights must follow users into the search so that a response never reveals a document that is inaccessible in the source system.
How to measure quality
A demonstration on a few simple questions is not enough. It is necessary to create a set of representative questions, specify the expected answers or sources, then separately measure the quality of the research and that of the formulation.
Errors must be classified: missing document, incorrect passage, insufficient context, ambiguous instruction or unfaithful generation. This distinction allows you to improve the right component rather than randomly changing models.
Frequent errors to avoid
Indexing all documents without governance quickly creates noise and contradictory responses. Another mistake consists of neglecting the tables, versions, dates and metadata which nevertheless give meaning to the contents.
Finally, a RAG must have an operational owner. Sources evolve, rights change and user questions reveal new needs. Quality therefore depends on a continuous improvement cycle.