Analytics Vidhya·
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.
Read full articleLearn 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.
I’ve seen a lot of promising AI prototypes fall apart after launch. And it’s rarely because the model was bad. More often, the problem starts much earlier; teams treat the data layer like something they can figure out later. They’ll spend weeks fine-tuning prompts, testing models and debating evaluation scores, then throw together the retrieval pipeline over a weekend and move on. At first, everything looks great in demos. But a few months later, the system gives outdated answers; the embeddings no longer match the source documents, and nobody fully understands what changed. What started as an impressive prototype slowly becomes difficult to trust in production. The teams that avoid this tend to realize one thing early: Embedding pipelines are fundamentally a data engineering problem, not an entirely new AI discipline. It’s still ETL (Extract, Load, Transform) at its core, but with embeddings and vector stores as the destination instead of a warehouse. Once you start looking at it that
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.
The NVIDIA AI Cloud ecosystem is accelerating the global buildout of AI factory infrastructure. Partners are expanding capacity to meet growing demand from enterprises, startups, nations, AI labs and developers scaling agentic AI applications. NVIDIA AI Clouds are a growing ecosystem of purpose-built clouds serving the exploding token demand behind today’s most popular AI applications. […]
Enterprise Document Intelligence [Vol. 1 #2] Why the same vector search that handles synonyms and paraphrase silently fails on negation, exact identifiers, and your company’s acronyms, and what to use when it does. The post Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval 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.
