Goodbye RAG? Google Finally Shipped Something useful! | Highlights and Annotations by Gistr.

The video explains why the author believes traditional Retrieval Augmented Generation (RAG) is obsolete due to advancements in large language models (LLMs) like Google's Gemini 2.0. Gemini's large context window (millions of tokens) allows it to process entire documents directly, eliminating the need for the chunking and embedding processes inherent in traditional RAG. While RAG remains relevant for managing extremely large numbers of documents, even then, the author advocates for parallel processing of smaller subsets of relevant documents with LLMs, rather than traditional RAG techniques. The author emphasizes the superior accuracy and reasoning capabilities of newer LLMs, making traditional RAG inefficient and unnecessary in many cases. This segment explains RAG, its historical importance due to limited context windows in LLMs, and how the advent of models like Gemini 2, with its significantly larger context windows (up to 2 million tokens), renders traditional RAG methods less necessary for single-document question answering. The evolution from small token limits requiring chunking and embedding to the ability to process entire documents directly is highlighted.The speaker contrasts the limitations of older LLMs with 4,000-token limits, requiring RAG techniques, with the capabilities of newer models possessing millions of tokens. This segment emphasizes the obsolescence of the traditional RAG approach for single documents due to the increased context window size and improved reasoning capabilities of modern LLMs, making direct processing more efficient and effective.This segment uses the example of a large transcript (e.g., an earnings call) to illustrate the limitations of traditional RAG. It explains how naive chunking in RAG prevents effective reasoning over the entire content, unlike newer LLMs that can directly process large amounts of text and provide more insightful answers to complex questions requiring reasoning. The segment contrasts the limitations of traditional RAG with the capabilities of directly feeding the entire transcript to a model like Gemini.The speaker clarifies that while traditional RAG for single documents is largely obsolete, the broader concept of RAG remains relevant when dealing with numerous documents. This segment explains that when faced with a large corpus of documents, a search-based approach to filter and select the most relevant documents is more efficient than attempting to process everything at once. The focus shifts to using modern LLMs to process the selected documents individually.This segment introduces the concept of parallelization as a more efficient approach when dealing with multiple relevant documents. Instead of chunking and embedding, the speaker suggests feeding each document separately to the LLM and then combining the results. This method leverages the power of modern LLMs and their low cost to achieve superior results compared to traditional RAG methods.The speaker details a robust system that involves using multiple LLMs in parallel to process selected documents, followed by merging their answers to provide a comprehensive response. This approach is presented as a superior alternative to traditional RAG, particularly given the low cost and high accuracy of modern LLMs. The segment concludes by summarizing the reasons why traditional RAG is considered obsolete in many scenarios.This segment offers practical advice for building AI products, emphasizing simplicity and the use of readily available tools. The speaker suggests starting with simple methods and only adding complexity when necessary. The segment also discusses the potential for future changes in the field, acknowledging that the landscape might evolve as LLMs become even cheaper and more efficient. The speaker reiterates the ineffectiveness of traditional RAG in many scenarios and advocates for the use of modern LLMs for improved results.