Question 1

What is RAG and why does it matter for enterprise GenAI?

Accepted Answer

Retrieval-Augmented Generation (RAG) grounds large language model responses in your actual data instead of relying on the model's training data alone. This reduces hallucinations, keeps answers current, and lets you control exactly which documents the model can reference. It is the difference between a chatbot that makes things up and one that cites your internal knowledge base.

Question 2

How do you measure RAG accuracy in production?

Accepted Answer

We set up eval dashboards that track retrieval precision (did the system find the right documents), answer faithfulness (does the response match the source), and relevance scoring (does the answer address the question). These metrics run continuously against a golden dataset of known-good Q&A pairs specific to your domain.

Question 3

What does "production-ready" mean for GenAI features?

Accepted Answer

It means the system handles real user traffic with acceptable latency, has safety gates preventing harmful outputs, includes usage analytics for cost monitoring, provides observability into model behavior, and has a tested rollback path. A demo that works on 10 queries is not production-ready. A system that handles 10,000 queries per day with 99.5% uptime is.

Question 4

How quickly can we launch a production RAG feature?

Accepted Answer

Allerin's GenAI Accelerator delivers a production-ready RAG system in 4 to 6 weeks. That includes data pipeline setup, retrieval tuning, eval framework, safety gates, and deployment with monitoring. The first working prototype is typically ready in week 2.

GenAI Product Accelerator

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