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Tagged: RAG

Every guide tagged "RAG", newest first.

Checklist contrasting when to pick a no-code chatbot builder and when to pick an AI trained on your own content
Chatbot fundamentals
64 views

A no-code chatbot builder lets you draw the conversation: menus, branches, forms. An AI trained on your own data reads your content and answers whatever is asked. They solve different problems, and picking the wrong one means either weeks of flow-building or a bot that wanders. This guide shows what each is good at, the real effort to build and maintain each, prices as of September 2026, and a decision list.

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Flow of a store chatbot: question, retrieval from catalogue and policies, answer with product cards, grade, content fix
Chatbot fundamentals
278 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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Store chat widget answering whether a backpack is waterproof, with a product card showing price and stock
Chatbot fundamentals
1,218 views

An AI chatbot for ecommerce answers shoppers from your own catalogue, policies and order data instead of from scripts. This guide explains how it differs from rule-based bots, live chat and generic AI chat, what it reads, what it does in a conversation, what it cannot do, how it installs on each platform, what it costs, and how to judge one in a ten-question trial.

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Pipeline of a RAG chatbot: question, retrieval over your content, generation from the passages, answer or a decline
Chatbot fundamentals
768 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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