The Chaotic, Honest History of the Engine Behind Google and Modern AI
I recently came across a delicious article on Medium that I had to share: Dense vs Sparse: A Short, Chaotic, and Honest History of RAG Retrievers by Pınar Ece Aktan. It's one of the best summaries I've read of the journey our field of Information Retrieval (IR) has taken.
With a perfect blend of humor and technical depth, it traces the evolution of search from the classic keyword-based systems like TF-IDF and BM25, through the rise of dense neural retrievers, and all the way to modern, sophisticated architectures like ColBERT. For anyone working in NLP or AI today, this article is more than just a history lesson; it's the story of the engine that powers a significant part of our digital world.
Why Information Retrieval is More Critical Than Ever¶
Reading this piece was a great reminder of just how paramount the field of Information Retrieval truly is. It's not merely a field within NLP; it is one of the foundational, commercially-driven pillars of the entire discipline.
For decades, the two primary applications of NLP that created massive enterprise value were search (IR) and classification. The recent, explosive boom in Large Language Models (LLMs) has not diminished the importance of IR—it has made it more critical than ever.
Today, Retrieval-Augmented Generation (RAG) is the dominant architecture for building factual, reliable, and enterprise-grade AI systems. The "retrieval" part of RAG is pure Information Retrieval. An LLM without a powerful retriever is just a brilliant but amnesiac conversationalist; a retriever gives it a long-term memory and a connection to factual reality. The work we do in this space—optimizing recall, improving ranking, and understanding relevance—is squarely at the center of this modern AI paradigm.
The Multi-Trillion-Dollar Problem¶
Is IR really behind the success of a company like Google? Absolutely, and it's impossible to overstate this.
Google's entire existence and its multi-trillion-dollar valuation are built on a revolutionary breakthrough in Information Retrieval: the PageRank algorithm. At its core, Google is an IR company. The article traces the very evolution of the technologies that Google has pioneered and deployed at a planetary scale. The journey from keyword matching to understanding semantic intent is the story of modern search, and it's the story of Google.
Pınar's article does a fantastic job of contextualizing this evolution. It connects the dots between the simple lexical baselines that many of us still use to benchmark our systems and the state-of-the-art neural architectures that we are now building and fine-tuning.
If you want to understand the "why" behind the work we do in RAG and see how it fits into a story that started decades ago and powers the biggest companies in the world, I highly recommend giving it a read.
Hope you enjoy it.