Answers with inspectable evidence
General model knowledge is insufficient when an employee asks about an internal procedure. RAG retrieves relevant information from external sources and places it in the model's response context. This connects answers to enterprise documentation that can be updated independently of model training.
Retrieval does not eliminate mistakes. An irrelevant passage or outdated procedure can still produce a misleading answer. A displayed citation must support the actual claim rather than merely give the response an appearance of authority.
Quality starts with the document
Begin with a small collection of approved documents. Give each an owner, review date, and validity status. Duplicates, unapproved drafts, and unreadable scans can weaken search quality. Cleaning this collection may help more than adding thousands of unexamined files.
Persian documents need checks for character variants, spacing, and complex tables. Splitting a paragraph or separating a figure from its column heading can change meaning. Compare retrieved passages with their originals to confirm that relationships survive preparation.
Enforce document permissions during retrieval. An assistant should not derive answers from material its user cannot read. Missing information and conflicting documents should be explained clearly, with clarification requested when the question is ambiguous.
Evaluate real employee questions
Collect common employee questions and assess retrieval relevance, answer correctness, and citation support separately. Include questions with no available answer. A dependable system must recognize insufficient evidence as well as provide useful responses.
Practical explanations and recommendations are Liyan Knowledge editorial analysis.Sources: Google Cloud — Retrieval-Augmented Generation · Google Cloud — Optimizing RAG Retrieval
This Liyan Knowledge article is an editorial synthesis based on the original source.View original source
