What RAG contributes
Retrieval-augmented generation finds relevant material and gives it to a model as context for a task. It can make a company answer easier to trace to sources, but it does not guarantee that the answer is complete or correct.
Choose and own the sources
Identify the document collection, source owner and approved users. The product has foundations for local material, Drive and SharePoint paths, each with its own configuration and permission semantics. Provider-specific policy and full lifecycle acceptance must be qualified.
Preserve access through retrieval
A vector index is derived search state, not an authority to disclose content. Check current collection and document permissions when retrieving material and when reading derived answers or shared history. A stale index must not revive revoked access.
Plan updates and deletion
Define how changed sources are ingested, how failed sync is reported and what happens to removed material. Retention of a record does not automatically grant access to the original document. Source references and retained content need clear policies.
Use different knowledge sets for different audiences
An internal research assistant and a customer-care agent may need different collections even when they share the same infrastructure. Keep the agent’s purpose, audience and business tools aligned with the material it may retrieve.
Connect through the API
The SDK query interfaces accept a question and retrieval options. Use a known collection where appropriate, and interpret streamed source information with the matching client/server contract. The company API address is separate from the model endpoint.
Define evidence and review
Keep the permitted source references, task context and observed result related to the AI work record. A citation is a route for review; a human still needs to decide whether it supports the answer.