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Retrieval-Augmented Generation, or RAG, has become the backbone of most serious AI systems in the real world. The reason is simple: large language models are great at reasoning and writing, but terrible at knowing the objective truth. RAG fixes that by giving models a live connection to knowledge. What follows are interview-ready question that could […]
Learn how integrating the Model Context Protocol (MCP) into SAS Retrieval Agent Manager transforms retrieval-augmented generation from a passive information system into a governed, scalable, and action-oriented enterprise AI platform capable of executing real business workflows. The post Unlocking agentic AI potential with MCP tools in SAS Retrieval Agent Manager appeared first on SAS Blogs.
Unlocking agentic AI potential with MCP tools in SAS Retrieval Agent Manager Read More »
Retrieval-Augmented Generation (RAG) technology almost immediately became the standard in intelligent applications. This was a result of the quickly developing field of artificial intelligence that combined large language models and external knowledge bases with different real-time access methods. RAG implementation of the traditional kind poses major difficulties: complex vector database setups, intricate embedding pathways, orchestration
NyRAG: Building Production-Ready RAG Applications with Zero Code Read More »
Most RAG demos stop at “upload a PDF and ask a question.” That proves the pipeline works. It doesn’t prove you understand it. These projects are designed to break in interesting ways. They surface bias, contradictions, forgotten context, and overconfident answers. That’s where real RAG learning starts. Once you’re through these, you would have an
10 RAG Projects That Actually Teach You Retrieval Read More »
Large Language Models (LLMs) such as GPT-4 and Llama 3 have affected the AI landscape and performed wonders ranging from customer service to content generation. However, adapting these models for specific needs usually means choosing between two powerful techniques: Retrieval-Augmented Generation (RAG) and fine-tuning. While both these approaches enhance LLMs, they are articulate towards different
