When Vector RAG Goes Blind: The $14B Note 12 Miss in Apple’s 2023 10‑K
Feb 27 · 4 min read · In the AI world of 2026, we have been sold a specific dream: Shred your PDF, turn the chunks into numbers (vectors), and let math find the answer. This is Vector RAG, and for simple FAQ bots, it works
Join discussion
How Search Engines Think: Understanding the Term–Document Index Matrix
Dec 18, 2025 · 3 min read · We have more data than understanding We live in a data-rich situation.Documents, webpages, PDFs, emails, and logs are created every second. But much of this data is never analysed at all. This creates a gap: Data is generated quickly Understanding ...
Join discussion
Not RAG, but RAG Fusion? Understanding Next-Gen Info Retrieval.
Sep 23, 2024 · 26 min read · Not RAG, but RAG Fusion? Understanding Next-Gen Info Retrieval. AI and Natural Language Processing are advancing at an incredible pace, and now more than ever, we need better and more RELIABLE ways to find and use information. As we've all experienc...
Join discussion
UDA - Unstructured Document Analysis
Jun 24, 2024 · 4 min read · UDA: A Benchmark Suite for Retrieval-Augmented Generation in Real-world Document Analysis Introduction In recent years, the use of Retrieval-Augmented Generation (RAG) has significantly enhanced the capabilities of Large Language Models (LLMs), enabl...
Join discussionSummarization Superpower: How Automatic Tools Can Help You Conquer Text Mountains
May 16, 2024 · 3 min read · What is text Summarization exactly? Text summarization is the process of distilling the key points of a text document into a shorter version while preserving the most important information. It's a crucial task in natural language processing (NLP) and...
Join discussion
How context window of LLMS cause hindrance in RAG apps
Apr 2, 2024 · 2 min read · A comprehensive overview of the challenges posed by restricted context windows in Retrieval-Augmented Generation (RAG) apps:. Token Limit and Context window in RAG: Large Language Models (LLMs): RAG models often rely on pre-trained LLMs for the gene...
Join discussion
Issues with RAG applications
Mar 30, 2024 · 1 min read · Retrieval-Augmented Generation (RAG) is a powerful technique, but it does come with some challenges: Finding Relevant Documents: The retrieval process is crucial, as RAG relies on identifying relevant documents to inform the generation process. If t...
Join discussion
Components of a RAG Application
Mar 28, 2024 · 2 min read · RAG (Retrieval-Augmented Generation) includes three main components: Embedding Model: This model takes textual information (queries, documents, etc.) and transforms them into numerical representations called "embeddings." These embeddings capture th...
Join discussionPopularity Bias in Recommender Systems
Feb 9, 2024 · 3 min read · Popular items are recommended even more frequently than their popularity would warrant The long-tail phenomenon is common in RS data: in most cases, a small fraction of popular items account for most user interactions When trained on such long-tail...
Join discussion