# What is RAG? Retrieval augmented generation, explained for business

RAG lets an AI answer from your documents instead of from general training data,
with a source attached. What it is good at, what it is bad at, and what it costs
to build.

==============================================================================
Author:     Daniellle Bhatt (https://www.linkedin.com/in/daniellle-bhatt-821998152)
Publisher:  AI Ready Marketing, https://aireadymarketing.com
Published:  13 August 2026
Updated:    13 August 2026
Canonical:  https://aireadymarketing.com/resources/what-is-rag
Reading:    8 minutes
Topic:      AI app development (https://aireadymarketing.com/services/ai-app-development)
Concepts:   RAG, Retrieval augmented generation, Vector database, Pinecone, Knowledge base
==============================================================================

## Where this comes from

We have built RAG systems in production, including an Airtable and Pinecone
knowledge hub with an automated ingestion pipeline that processed around 360
documents in a single run with zero failures. The failure modes described here
are ones we hit and had to design around.

## Key points

- RAG makes an AI answer from your own documents, with a citation, instead of
from general training data.

- It is the right choice when answers must be grounded in your material and
traceable to a source.

- The hard part is never the AI. It is the state of your documents.

- A useful internal RAG system typically costs $10,000 to $25,000 to build, plus
$50 to $200 a month to run.

## Introduction

Retrieval augmented generation is a way of making an AI answer from a specific
set of documents rather than from everything it absorbed during training. When
someone asks a question, the system first retrieves the most relevant passages
from your material, then asks the model to answer using only those, and returns
the answer with its sources attached.

## Why it exists

A general model knows a great deal about the world and nothing about your
business. It has never seen your pricing, your contracts, your product
specifications or last quarter's board pack. Asked about them, it will either
say it does not know, or produce something plausible and wrong.

RAG closes that gap without retraining anything. Your documents stay where they
are; the system just gets very good at finding the right passage before
answering.

## How it works, in four steps

The citation step is what makes it usable in a business. An answer you can trace
is an answer someone will act on.

- Ingest. Documents are split into passages and converted into vectors, which
are numerical representations of meaning.

- Store. Those vectors go into a vector database such as Pinecone, or a database
with vector support.

- Retrieve. A question is converted the same way, and the closest passages are
pulled back. This is matching by meaning, not by keyword, so "what is our refund
window" finds a passage about returns policy.

- Generate. The model is given those passages and asked to answer using them,
citing which one it used.

## What it is good and bad at

Good at | Bad at
--- | ---
Answering from a defined body of documents | Questions needing a full-corpus view, like "summarise every contract"
Attaching a source to every answer | Arithmetic across many documents
Staying current, since you update documents not models | Anything where the documents are contradictory or out of date
Keeping proprietary material out of a public model | Judgement calls the documents do not cover

## The part that actually takes the time

Your documents. Every RAG project is a data project wearing a costume. Duplicate
policies, three versions of the same price list, scanned PDFs with no text
layer, and knowledge that only exists in somebody's head.

The system will faithfully retrieve the outdated price list if the outdated
price list is what it was given. Garbage in, confidently cited garbage out,
which is worse than no answer because it looks authoritative.

Budget real time for document cleanup. On most projects it is the largest single
line item, and skipping it is the most reliable way to produce a system nobody
trusts.

> A RAG project is a data project wearing a costume. If your documents are a mess,
> the answer is to fix the documents, not to buy a better model.

## What it costs

A useful internal system typically runs $10,000 to $25,000 to build, depending
mainly on how much document preparation is required. Running costs are modest:
$50 to $200 a month for the vector database and model usage at normal internal
volumes.

Worth it when several people repeatedly ask the same questions of a large
document set: support teams, sales teams answering technical questions, or any
business where the answer exists somewhere and finding it takes twenty minutes.

Not worth it for a small document set. If it fits in a well-organised folder
that ten people search occasionally, improve the folder. More on that decision
in build or buy.

## Sources

1. Claude, Anthropic. https://www.anthropic.com/claude
2. ChatGPT, OpenAI. https://openai.com/chatgpt

------------------------------------------------------------------------------
AI Ready Marketing is an AI transformation partner based in Melbourne,
Australia, building AI systems across marketing, sales and operations. This
article is published in full at
https://aireadymarketing.com/resources/what-is-rag and may be quoted with
attribution to Daniellle Bhatt, AI Ready Marketing.
Contact: hello@raamaimarketing.com · Book a call: https://aireadymarketing.com/book