How would you integrate LLM-powered semantic search into a SaaS platform? Explain your embedding strategy, latency optimization, and safety considerations.

I’d first convert the data into embeddings and keep it in a vector DB, then when a question comes I just pull the most relevant chunks, clean them up with re-ranking, and give only that to the model so it doesn’t talk nonsense. To keep it fast, I’d precompute stuff, cache common queries, and keep everything lightweight. And for safety, I’d lock data per user, strip PII, and add guardrails so nothing weird or leaked ever comes out.
For hallucinations, I’d make the model answer only from retrieved, trusted data, use re-ranking and confidence checks, and block answers when the context isn’t strong enough. For latency, I’d precompute embeddings, cache common results, use fast ANN search with small models, and keep the pipeline async so responses stay fast at scale.

1. Overall Architechture that can be SaaS+LLM ingestion services to process documents or entities

2. Query servie that takes user's natural language query, creates embedding runs approximate nearest neighbour (ANN) and also Multi vector