Browsing: RAG

In this article, you will learn the mechanical difference between retrieval-augmented generation and fine-tuning, when each technique is the right…

IntroductionRetrieval-augmented generation (RAG) answers questions using information retrieved from a set of documents. That document collection is called a corpus.…

Over the last year, I’ve lost count of the number of conversations I’ve had about AI agents, RAG, semantic layers,…

The number you need sits in a table, at the intersection of a row and a column. Flatten the PDF…

The most useful answer a RAG system can give is sometimes “that is not in this document.” But when asked…

A FAQ is already the answer, pre-written and paired with its question. Ask “What is my deductible?” and the right…

Retrieval-Augmented Generation (RAG) architectures have expanded considerably beyond the original retrieve-and-generate pattern over the last few years. Contemporary systems increasingly…

The user types “assurance décénale” and the document says “décennale.” One missing letter, and a literal search finds nothing. Real…

A support request lands in the queue, and something has to work out what it is about before anyone can…

When RAG retrieval disappoints, the advice AI engineers hear today is almost always “add a reranker”. Ask why a reranker…