Retrieval-Augmented Generation (RAG) enhances the accuracy of AI responses by retrieving relevant information from external knowledge sources. But building an effective RAG pipeline isn’t as easy as it sounds. From poor retrieval quality to slow sear...
Large Language Models (LLMs) are AI systems trained on massive amounts of text to understand and generate human‑like language. They are widely used in business applications—from drafting emails and generating marketing copy to powering chatbots and v...
Artificial Intelligence, or more accurately, Machine Learning has been a hot topic for the past few years, and rightfully so. With the rise of LLMs (Large Language Models), how we search, create, and communicate has fundamentally changed. From asking...
Introduction: The RAG Hype is Real, But So Are Its Failures Retrieval-Augmented Generation (RAG) has taken the AI world by storm, promising to anchor Large Language Models (LLMs) in factual, up-to-date, and proprietary data. The concept is elegant: r...
Introduction If you're reading this, you probably know what LangChain is, right? We all recognize it's a great tool for creating RAG pipelines and multi-AI agents. However, after working on multiple projects using LangChain and LangGraphs, I realized...
For some months now, I’ve been immersed in wrapping up my master’s thesis, which involved scraping thousands of book descriptions from various sources (a tedious process that still gives me nightmares!). While the work was exhaustive, a thought kept ...
The landscape of artificial intelligence is rapidly evolving, and one of the most transformative breakthroughs in recent years is the Retrieval-Augmented Generation (RAG). Traditional large language models (LLMs) have demonstrated impressive abilitie...
In a world driven by data, making smart, real-time decisions is critical. At ProsperaSoft, we help you unlock the full power of your AI systems through Retrieval Augmented Generation (RAG) — a cutting-edge approach that enhances AI accuracy, speeds u...
As impressive as they are, large language models (LLMs) have a critical limitation: they can’t know what they haven’t learned. This means that recent or domain-specific information — like the latest monetary policy decisions from the FED — simply doe...
INTRODUCTION RAG, or Retrieval-Augmented Generation, is an AI framework that combines the strengths of traditional information retrieval systems with the abilities of generative large language models (LLMs). It lets LLMs use and include current, exte...
