What RAG changes
Retrieval-augmented generation, or RAG, gives an AI system relevant passages from approved documents before it creates an answer. This helps the system answer from current business knowledge instead of relying only on general model training.
RAG does not guarantee correctness. The value depends on document quality, chunking, metadata, retrieval, permissions, prompting, and evaluation.
Prepare the knowledge before indexing it
Uploading every file into one index usually creates noisy results. Prepare a clear source set first.
- Remove obsolete, duplicated, and contradictory documents
- Assign owners, dates, categories, versions, and access permissions
- Extract clean text from PDFs, pages, tables, and structured systems
- Split content in a way that preserves the meaning of each policy or procedure
Build for trustworthy answers
The assistant should cite its supporting sources, state when evidence is missing, and avoid answering beyond its approved scope. Permission filters should run before results are shown to the model or user.
For high-impact questions, the system can retrieve information and prepare a response while requiring a knowledgeable person to approve it.
Evaluate with real questions
Create a test set from questions employees or customers actually ask. Include answerable questions, ambiguous questions, outdated terms, restricted information, and questions with no approved answer.
Measure whether the correct source was retrieved, whether the answer is supported by that source, whether citations are useful, and whether the system appropriately refuses unsupported requests.