Towards Data Science·
Increasing context size in RAG systems doesn’t improve accuracy for aggregation tasks—it makes errors harder to detect. In this article, I benchmark retrieval-based pipelines against a deterministic full-scan engine across 100,000 rows and show why computation queries must be routed away from RAG entirely. The post Larger Context Windows Don’t Fix RAG — So I Built a System That Does appeared first on Towards Data Science.
Read full articleJune 17, 2026 — Amazon Bedrock Managed Knowledge Base, a fully managed retrieval-augmented generation (RAG) service, is now generally available. With Managed Knowledge Base, developers can build production-ready AI agents grounded […] The post AWS Launches Amazon Bedrock Managed Knowledge Base for Enterprise RAG Applications appeared first on AIwire.
First came vector databases, then RAG. Now, the next frontier in enterprise AI is taking shape: context layers that give autonomous agents a shared understanding of the business, a vision Databricks is advancing with Genie Ontology. Currently in preview, Genie Ontology automatically extracts business context from enterprise data, dashboards, queries, pipelines, documents, and applications and organizes it into a living graph that AI agents can use to understand how an organization operates. Showcased at the company’s Data + AI Summit, Genie Ontology uses a ranking system inspired by Google’s PageRank to identify the most authoritative business definitions within an organization. Rather than treating all sources equally, it weighs factors including who created the information, how widely it is used, its links to certified datasets and assets, and how recently it was updated before determining which answer an AI agent should rely on, Databricks CEO Ali Ghodsi said during his keynote late
Enterprise Document Intelligence [Vol.1 #6a] - Why a user question deserves the same parsing as the document, and how it splits into a retrieval brief and a generation brief before either runs The post RAG Questions Need Parsing Too: Turn the User’s String Into Briefs for Retrieval and Generation appeared first on Towards Data Science.
Enterprise Document Intelligence [Vol.1 #5quater] - The other parsers read the words on a page. A vision model also reads the pictures The post Vision LLMs are PDF Parsers Too: Reading Charts and Diagrams for RAG appeared first on Towards Data Science.
Enterprise Document Intelligence [Vol.1 #5ter] - Table cells, OCR, captions, headings: cloud-grade structure, running on your own machine. No key, no per-page bill, nothing leaves the building The post Parse PDFs for RAG Locally with Docling: Rich Tables, No Cloud Upload appeared first on Towards Data Science.
Learn how the SAS Agentic AI Accelerator and SAS Viya can be used to build a governed, multi-agent support-ticket solution that combines text analytics, RAG, LLMs, business rules, and human oversight to improve resolution speed, accuracy, and operational efficiency. The post Modernizing attendance ticketing in SAS Viya using SAS Agentic AI Accelerator appeared first on SAS Blogs.
Enterprise Document Intelligence [Vol.1 #4bis] - A coauthor note on the brick-by-brick pitfalls that justified the four-brick split, before Part II walks the fixes The post 10 Common RAG Mistakes We Keep Seeing in Production appeared first on Towards Data Science.
Modern AI applications rely on understanding meaning rather than matching keywords. As large language models, semantic search, and RAG systems have become mainstream, vector databases have emerged as critical infrastructure for storing and retrieving high-dimensional embeddings at scale. Choosing the right vector database can have a major impact on performance, scalability, cost, and developer experience. […] The post Choosing the Right Vector Database for RAG and AI Applications appeared first on Analytics Vidhya.
