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Retrieval-Augmented Generation

RAG Chatbot for
Your Website

RAG (Retrieval-Augmented Generation) helps AI answer from your own content instead of memory. ChattyBox gives you a managed retrieval flow for websites, docs, CMS pages, and help centers.

What is RAG?

RAG combines a language model with retrieved source content. Before generating a response, the chatbot searches your published pages for passages that match the question.

1

Retrieve

When a visitor asks a question, ChattyBox searches indexed website, docs, help, and CMS content for relevant passages.

2

Augment

Those source passages are added as context so the model has something specific to answer from.

3

Generate

The model generates a concise answer and can include source links so visitors can verify the details.

How ChattyBox keeps retrieval practical

Searchable Content Index

Your website, docs, help center, and CMS pages are indexed so the chatbot can search the content you already publish.

Embeddings Model

Embeddings help match visitor questions to relevant source passages even when the wording is different.

Focused Retrieval

For each question, ChattyBox retrieves a small set of relevant passages instead of asking the model to answer from memory.

Context-Limited Prompting

The answer step is constrained to the retrieved context and configured to avoid unsupported claims.

Citation Links

Every response includes links to source pages, so users can verify the information.

Fallback Handling

When relevant content is missing, the bot can fall back instead of improvising an unsupported answer.

RAG without rebuilding your content stack

Connect your existing site content, test answer quality, and embed a source-cited chatbot when the responses look right.

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