Ingest and chunk
Source documents are split into passages and each is embedded into a vector database.
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[ glossary — ai architecture ]
A plain-English guide to RAG for founders, developers, and product teams evaluating AI chatbots, search, and knowledge systems grounded in their own data.
200+
products shipped
4.9/5
Upwork rating
15+
countries served
30 days
post-launch support
( how it works )
Source documents are split into passages and each is embedded into a vector database.
At query time the system finds the most relevant chunks using semantic search.
The LLM writes an answer from the retrieved context, so output can cite your sources.
Chunking, retrieval ranking, and prompt design are tuned against a held-out question set.
( where it fits )
Answer support questions with your docs, tickets, and product knowledge.
Give employees search over policies, wikis, and operating procedures.
Find answers across manuals, research, legal, and field data with citations.
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 )
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.
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.
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.
Udaan typically scopes RAG systems from $6,000 to $30,000 depending on document volume, retrieval quality, integrations, and agentic features.
Yes, when engineered well. RAG reduces hallucination and keeps answers grounded in sources, but retrieval quality and chunking design are what determine accuracy.
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.