Modernizing attendance ticketing in SAS Viya using SAS Agentic AI Accelerator

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.

Explore how SAS AI-Driven Entity Resolution on SAS Viya combines no-code configuration, probabilistic matching, and industry-specific integrations to create trusted identities, improve data quality, and support critical decisions across sectors such as public services and financial crime prevention. The post From entity resolution to industry solutions: How AI‑driven entity resolution is evolving on SAS Viya appeared first on SAS Blogs.

A quick search through headlines reveals a range of AI-related disappointments. Consider that 95% of GenAI pilots fail, according to MIT. Amazon’s Kiro agent recently sparked a 13-hour outage by deleting a production environment. And we can’t forget that the resource and energy strain from a new wave of AI [...] The post Reviving the promise of AI with RAG, data and agentic appeared first on SAS Blogs.

Enterprise Document Intelligence [Vol.1 #3] - Why the ML toolkit (hyperparameter sweeps, train/test splits, explainability frameworks) solves the wrong problem, and what to use instead The post RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem appeared first on Towards Data Science.

Enterprise Document Intelligence [Vol. 1 #1] The smallest version of RAG that actually works, on a real PDF, with grounded answers and the source lines highlighted. The post Baseline Enterprise RAG, From PDF to Highlighted Answer appeared first on Towards Data Science.

This post explores how Agentic AI and LLMs can help reduce employee attrition by delivering personalized development guidance based on workforce analytics and skill profiling. Using SAS Viya and governed AI workflows, the solution matches employees with tailored learning opportunities while supporting transparent, scalable, and data-driven workforce planning. The post Agentic AI for Workforce Analytics: Reducing attrition with personalized, LLM-powered guidance appeared first on SAS Blogs.

Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking, achieving an 85% reduction in LLM costs without sacrificing answer quality. The post RAG Is Burning Money — I Built a Cost Control Layer to Fix It appeared first on Towards Data Science.