Ingest and chunk
Source documents are split into passages and each is embedded into a vector database.
[ 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.
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post-launch support
( how it works )
Retrieve first, then answer with proof.
Retrieve relevant context
At query time the system finds the most relevant chunks using semantic search.
Generate grounded answers
The LLM writes an answer from the retrieved context, so output can cite your sources.
Iterate on quality
Chunking, retrieval ranking, and prompt design are tuned against a held-out question 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 memory
Ground AI agents in live business data so they act on current facts.
( comparison )
| Approach | How it works | Best for |
|---|---|---|
| RAG | Retrieve data at query time from a corpus | Frequent updates, citeable answers, no retraining |
| 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 )
Questions buyers ask
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 typically includes ingestion (chunking and embeddings), a vector database for search, an LLM for generation, and orchestration logic that retrieves and formats context.
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?
Yes, when engineered well. RAG reduces hallucination and keeps answers grounded in sources, but retrieval quality and chunking design are what determine accuracy.
When should a company build RAG?
Build RAG when you have a growing corpus, need citeable answers over your own data, and expect frequent content updates that retraining cannot keep up with.