How the lead practice actually starts
RAG vs fine-tuning: what we ship first from Gurugram
Retrieval-augmented generation answers from your documents. Fine-tuning changes the model. Most Indian products need the first. Here is how we choose.
Updated 2026-09-19 · CycloneWebz, Gurugram
RAG is a product pattern, not a buzzword
A RAG assistant retrieves passages from your help centre, PDFs, tickets, or policies, then answers with those passages in context — usually with a citation and a way to talk to a human. The model does not need to “know” your business. It needs to find the right page.
That is why RAG chatbot development is the first release we recommend for most Gurugram and remote briefs. The corpus is yours. The answers can be audited. When a policy changes, you update a document instead of retraining a model.
When fine-tuning is the job
Fine-tuning is worth it when the task is a style or a classification the base model keeps missing, and you have clean labelled examples. It is the wrong first step when you do not yet have the documents, the owners, or the evaluation set.
We will say which, in writing. Many “we will train your own LLM” pitches are a longer road to the same retrieval product — with more lock-in.
What we refuse to invent
We do not put a public model on a private corpus without agreeing store, vendor, and retention. We do not claim lift percentages we cannot measure. The in-product AI assistant study on this site is the shape of the work: problem, build, deliverables.
Questions this guide usually raises
Is RAG cheaper than training a model?
Usually, for the first production release. You pay for retrieval quality, evaluation, and the product around the answers — not GPU weeks. Training becomes cheaper only when you already have labels and a reason the base model fails.
Can RAG run on WhatsApp?
Yes. The retrieval layer is the same; the session design and the fallback are not. We ship WhatsApp assistants when that is the channel people already use.
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