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Anish Korat

How chatbots work · Vatdi team

Anish explains how retrieval-based chat works in plain words: what the assistant reads, why it can say "I don't know", how languages are detected, how to test accuracy, and where it still fails. He cites the research and vendor documentation rather than borrowed statistics.

Flow of a store chatbot: question, retrieval from catalogue and policies, answer with product cards, grade, content fix
Chatbot fundamentals
165 views

Nine techniques that move a store chatbot from "answers something" to "answers correctly": grounding every reply in retrieved content, structuring the catalogue so attributes are findable, writing policy pages a retriever can use, steering with quick replies, handover rules, per-language testing, coupon conditions, guardrails on prices and promises, and a 30-question quality loop you re-run after every content change.

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Pipeline of a RAG chatbot: question, retrieval over your content, generation from the passages, answer or a decline
Chatbot fundamentals
620 views

A RAG chatbot answers in two steps: it retrieves the passages of your own content that match the question, then writes an answer from those passages only, declining when nothing matches. This guide walks one question through both steps, explains chunks, embeddings and keyword search in plain words, lists the ways retrieval still fails on a real store and the content fix for each, and shows how to test it.

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Chat widget answering a French shipping question and a Portuguese follow-up in each visitor's language
Chatbot fundamentals
561 views

A multilingual AI chatbot does four separate things: detects the language of each message, retrieves the answer from content that may be written in another language, writes the reply in the visitor's language, and switches the widget's own labels. This guide explains how each step works, where it breaks (short messages, product names, mixed-language chats), what you configure, and how to test it before launch.

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