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As AI systems reshape the way we interact with information, Retrieval-Augmented Generation (RAG) has emerged as a powerful architecture to combine internal knowledge with generative language models. But with great capabilities comes a critical challenge: how do we know if a RAG system works?

This blog post outlines a structured, real-world approach to validating a RAG system, based on our experience building and maintaining COGNOS, a RAG-powered product at Apiumhub.

What Is a RAG System?

A RAG system combines an information retrieval engine (e.g., a search or vector database) with a generative large language model (LLM), allowing it to answer questions based on both proprietary data and external general knowledge.

Why Is RAG Validation Difficult?

Unlike traditional rule-based systems, RAG systems are non-deterministic. Answers depend on:

The model you use The documents retrieved The phrasing of the input question

In our case with COGNOS, the challenge is even

To read the full article click on the 'post' link at the top.