Author: Henri Blondelle

Imagine trying to have a meaningful conversation about a library’s worth of technical documentation.

This is the reality many organizations face when implementing AI-powered document chat solutions.

Our initial approach at AgileDD addresses this challenge through intelligent metadata extraction.

By automatically capturing and indexing key information from documents, we enable more precise retrieval before a single question is asked.

But what if we could go further?

What if we could transform the document text itself to make it more digestible for AI systems?

When building a chatbot that needs to process tens of thousands of technical documents, data scientists love to discuss sophisticated approaches — but there’s one critical algorithm they’re often hesitant to spotlight – Retrieval-Augmented Generation, or RAG.

Let’s explore what makes RAG so valuable, how it works, and how we’re making it more effective at AgileDD.