Question 1

Which vector database should we use?

Accepted Answer

It depends on your requirements. Pinecone for fully managed with zero ops overhead. Weaviate for hybrid search built-in and self-hosted flexibility. Qdrant for performance-critical workloads (Rust-based, fastest benchmarks). pgvector if you want to keep everything in PostgreSQL. Milvus for massive scale (10B+ vectors). We recommend starting with managed (Pinecone/Weaviate Cloud) and moving to self-hosted as data volume grows.

Question 2

How do you handle documents with tables and complex layouts?

Accepted Answer

We use LlamaParse for LLM-powered document parsing that understands table structures, forms, and multi-column layouts. For simpler documents, Unstructured.io handles 20+ formats. Tables are extracted as structured data (not flattened text) and stored as separate retrievable objects. Engineering drawings and diagrams are processed with multimodal models (GPT-4o vision) for contextual understanding.

Question 3

What retrieval precision do your RAG systems achieve?

Accepted Answer

Our production systems typically achieve 92-97% context precision (the right documents are retrieved) and 88-95% faithfulness (the LLM answer is grounded in retrieved context), measured with RAGAS. This is achieved through hybrid search + reranking + quality filtering. We run RAGAS evaluations continuously in production, not just during development.

Question 4

How do you prevent data leakage between tenants?

Accepted Answer

Multi-layer isolation: (1) namespace-level separation in vector databases - tenants can't query each other's namespaces, (2) metadata-based access control on every document chunk, (3) separate API keys and authentication per tenant, (4) query-time filtering that enforces permissions before results are returned, (5) audit logging of every query with tenant context. We validate isolation with automated penetration tests.

Question 5

Can RAG work with real-time data that changes frequently?

Accepted Answer

Yes. We build incremental ingestion pipelines that update the vector index in near-real-time. For frequently changing data (knowledge bases, wikis, Confluence), we use webhook-triggered re-indexing. For databases, we implement CDC (Change Data Capture) to stream updates to the vector index. Cache invalidation ensures stale results are never served.

Question 6

How do you measure and improve RAG quality over time?

Accepted Answer

We deploy RAGAS evaluation in a continuous monitoring loop: faithfulness (is the answer grounded in context?), answer relevancy (does the answer address the question?), context precision (are the right docs retrieved?), and context recall (are all relevant docs retrieved?). Metrics are tracked in dashboards. When quality drops below thresholds, we investigate - often the fix is better chunking, updated embeddings, or reranking tuning.

Question 7

What's the cost of running a production RAG system?

Accepted Answer

Main costs: vector database ($100-500/month for managed, self-hosted is cheaper), embedding generation ($0.10-0.30 per 1M tokens), LLM calls for answer generation ($5-15/1M tokens for GPT-4o), and compute for ingestion pipelines. With semantic caching, costs drop 30-50% for repetitive queries. A typical enterprise RAG system serving 10,000 queries/day costs $2,000-5,000/month all-in.

RAG Systems
Enterprise Knowledge Retrieval That Eliminates Hallucinations

LLMs Without Retrieval Hallucinate.

Production-Grade Tools We Deploy

Vector Databases

Embedding Models

Orchestration

Chunking & Parsing

Reranking

Search Infrastructure

Evaluation

Caching & Infrastructure

How We Build It

Advanced Retrieval Patterns

Chunking & Ingestion Strategies

Reranking & Quality Pipeline

Multi-Tenant Knowledge Isolation

Data Security, Governance & Safety

Data Sovereignty

Model Safety & Guardrails

Access Control & Audit

Responsible AI

Frequently Asked Questions

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RAG SystemsEnterprise Knowledge Retrieval That Eliminates Hallucinations