Ingest and chunk
Prepare source documents and their access rules, then index useful passages for search.
[ glossary — ai architecture ]
What is Retrieval-Augmented Generation?
A plain-English guide to RAG for founders, developers, and product teams evaluating AI chatbots, search, and knowledge systems grounded in their own data.
( how it works )
Find context, then check the answer.
Retrieve relevant context
Find candidate passages using keyword, semantic or hybrid search, with permissions enforced.
Generate grounded answers
Ask the model to use the retrieved evidence and provide source references that can be checked.
Iterate on quality
Use development questions to improve retrieval and prompts, then check quality on a separate evaluation set.
( where it fits )
Use RAG for knowledge-heavy AI products.
Support copilots
Answer support questions with your docs, tickets, and product knowledge.
Internal Q&A
Give employees search over policies, wikis, and operating procedures.
Document search
Find answers across manuals, research, legal, and field data with citations.
Agent context
Supply relevant business information to an agent, with freshness checks and limits on what it may access.
( comparison )
Compare ways to supply knowledge
Scroll across to see all columns.
| Approach | How it works | Best for |
|---|---|---|
| RAG | Retrieve source material for the prompt | Large or changing source collections and traceable evidence |
| Fine-tuning | Update model weights on your data | Style, tone, and domain behavior |
| Prompting | Inject knowledge in the prompt context | Small, stable knowledge sets |
( faq )
Common questions
What does RAG stand for?
RAG stands for Retrieval-Augmented Generation. It combines a retrieval step, which finds relevant information, with a generation step, which writes an answer using that information.
How is RAG different from fine-tuning?
Fine-tuning updates the model's weights on your data; RAG retrieves and injects data at query time. RAG is faster to update and easier to trace, while fine-tuning changes style and behavior.
What are the main components of a RAG system?
A RAG system prepares and indexes sources, retrieves relevant content, and provides it to a language model. Retrieval may use keyword, semantic or hybrid search. Access controls, source updates and evaluation are also needed for a production system.
How much does a RAG system cost?
Udaan typically scopes RAG systems from $6,000 to $30,000 depending on document volume, retrieval quality, integrations, and agentic features.
Does RAG improve answer quality?
Relevant evidence can improve an answer, but poor retrieval or incorrect generation can still produce errors. Evaluate both retrieval and answers, check citations, and define what happens when the sources do not support a response.
When should a company build RAG?
Consider RAG when users need answers from a source collection that is too large to include directly in a prompt or changes regularly. First check source quality, access permissions and how answers will be evaluated.