This use case showcases how we used Zammo Agents to quickly build and deploy an Agent powered by Retrieval-Augmented Generation (RAG). The goal was to create an Agent that could accurately answer questions based on the content of our website—and cite its sources.

Overview

Using only natural language prompts in the Zammo Agent Designer, we built a fully functional Agent that pulls answers from an indexed knowledge base tied to our live website content. This example demonstrates how easily organizations can create RAG-powered experiences without writing code or managing complex integrations.

What We Did

✅ Indexed Website Content

We began by creating a Zammo Knowledge Base and indexing content from the Zammo homepage. The indexed content becomes the foundation from which the Agent draws relevant answers.

Zammo homepage

Zammo knowledge base containing content indexed from the homepage

✅ Connected Content via a RAG Project

Next, we created a RAG Project in Zammo. This step links the knowledge base to retrieval logic that enables the Agent to generate context-aware answers and include source citations.

✅ Created the Agent with Natural Language

In Agent Designer, we selected the FAQ knowledge base predefined experience.

We selected the RAG Project to use.