Is RAG Dead? Rethinking AI Product Development with Gemini 2.0

Introduction

Google's Gemini 2.0 Flash is making waves, and it's got some folks rethinking how we build AI products. A recent X post suggesting to replace your RAG pipeline with just 20 lines of code begs the questions "is RAG needed anymore?", so let's break down what that actually means.

What's RAG Anyway?

For those unfamiliar, RAG stands for Retrieval Augmented Generation. It's a technique used to give large language models (LLMs) like ChatGPT access to information outside of their training data. Think of it like giving a student their textbook and notes before an exam.

Traditionally, LLMs had limited "context windows," meaning they could only process a small chunk of text at a time. So, if you wanted an LLM to answer questions about a large document (like a PDF), you'd have to "chunk" the document into smaller pieces, create "embeddings" (numerical representations of the text), and store them in a vector database. When a user asked a question, you'd find the most relevant chunks and feed those to the LLM.

Gemini 2.0 Massive Context Windows

The game has changed! Models like Gemini 2.0 Flash have massive context windows – we're talking millions of tokens! This means you can feed entire documents (or even multiple documents) to the LLM without chunking. This simplifies things drastically. Remember those 20 lines of code to do all that chunking and embedding? Now you can often just give the LLM the document directly.

When Does RAG Still Matter?

Okay, so chunking single documents is often unnecessary now. But what about when you have tons of documents? Let's say you're an analyst trying to understand Apple's performance and have years' worth of earnings reports. You're not going to feed all of that to the LLM at once!

This is where a broader sense of RAG comes into play. You'll likely use search (or a similar method) to filter down to the most relevant documents (e.g., the NVIDA earnings reports). Then, instead of chunking those reports, you can feed each report separately to the LLM.

The Power of Parallelization

In a scenario where one has multiple files to query, such as transcripts of the annual earnings calls from 2020 to 2024, a useful technique to consider is parallelization.

Rather than chunking the documents, you can send each one to its own LLM, posing the same question multiple times—once for each relevant document. Afterward, you can combine the LLM's responses to formulate a final, more comprehensive answer from a new query to reason over the answer. This approach leverages the LLM's reasoning abilities across the entire document, resulting in significantly improved outcomes compared to chunking.

The Takeaway

Traditional RAG (chunking single documents) is becoming obsolete thanks to massive context windows. But a broader concept of RAG (using search to filter documents and then leveraging LLMs' reasoning across full documents) is still very relevant, especially when dealing with large datasets. The key is to keep things simple and avoid overcomplicating things unless absolutely necessary. The AI landscape is constantly evolving!