Question 1

What is RAG (Retrieval-Augmented Generation)?

Accepted Answer

RAG is a technique that retrieves relevant context from your company's private documents and feeds it to an LLM, ensuring the model answers based on factual data rather than generic public knowledge.

Question 2

How do you guarantee that the AI won't hallucinate?

Accepted Answer

We enforce strict system prompt guardrails. If the retrieved context does not contain the answer, the AI is instructed to reply that it cannot find the information, preventing false claims.

Question 3

What vector databases do you support?

Accepted Answer

We deploy solutions on Pinecone for managed cloud setups, Weaviate for hybrid enterprise environments, and Pgvector (PostgreSQL) or Chroma for local, lightweight deployments.

Question 4

Can it index documents stored in Google Drive or SharePoint?

Accepted Answer

Yes, we build automated connector pipelines that pull documents from Google Drive, SharePoint, Notion, Confluence, and corporate databases securely.

Question 5

How are source citations displayed?

Accepted Answer

We track document metadata throughout retrieval. Every generated answer includes numbered footnotes linking to the exact document name, page number, and original file link.

Question 6

How do you handle document security and user permissions?

Accepted Answer

We implement retrieval-time filtering. The system checks the user's active permissions group (e.g., via OAuth) and only retrieves context from documents the user is authorized to read.

Question 7

Do you support hybrid search?

Accepted Answer

Yes, we combine sparse keyword search (BM25) with dense semantic vector search. This ensures search queries find both exact matches (like serial numbers) and conceptual matches.

Question 8

How often is the vector index updated?

Accepted Answer

We set up webhook endpoints that listen for document changes in real time, or run incremental synchronization tasks every hour to ensure the search index is always updated.

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