What is RAG in simple terms?

RAG (Retrieval-Augmented Generation) is a way to make AI answer questions using your specific business documents instead of guessing. It retrieves relevant info from your docs first, then generates an answer based on that info — with source citations.

RAG vs fine-tuning: which is better?

For most business use cases, RAG wins. It's cheaper, faster to deploy, easier to update, and far less prone to hallucinations. Fine-tuning is only better when you need the AI to fundamentally change its behavior, not just learn new facts.

How accurate can RAG be?

A well-tuned production RAG system hits 90%+ accuracy on representative business questions. Below 90%, the issues are almost always chunking, retrieval, or prompt engineering — not the LLM itself.

What does a RAG project cost?

For a custom built RAG: ₹50K–₹3L one-time build + ₹5K–₹50K/month hosting depending on query volume and complexity.

How long to deploy RAG in production?

5-7 days for most use cases. Larger or more sensitive deployments (legal, healthcare with PHI) may take 2-3 weeks for proper compliance setup.

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RAGFoundationsCornerstone

What is RAG? A No-Bullshit Guide for Founders (2026)

Praful Thakkar

Co-Founder & CTO, AI Agent Mindset

· April 15, 2026 · 7 min read

TL;DR

RAG = Retrieval-Augmented Generation. It's how you make an LLM answer questions about *your* business without it making things up.

The flow:

1. Your docs (PDFs, contracts, SOPs, product specs) get chopped into chunks.

2. Each chunk gets converted into a numerical "embedding" (vector).

3. Stored in a vector database (Pinecone, Qdrant, etc.).

4. When a user asks a question, the system finds the most relevant chunks.

5. Those chunks + the question go to the LLM (Claude, GPT-4).

6. The LLM answers based on the retrieved chunks — with citations.

Result: an AI that *actually knows* your business, with no hallucinations.

Why RAG matters

LLMs are trained on the internet up to some cutoff date. They don't know your pricing, your contracts, your SOPs, or your product specs. If you ask ChatGPT "what's your refund policy?", it'll either guess (badly) or refuse to answer.

You have two ways to fix this:

Fine-tuning — re-train the LLM on your data. Expensive (