Sep 27, 2025 · 10 min read · Retrieval-Augmented Generation (RAG) systems have revolutionized how we build AI applications, but they're far from perfect. While RAG can provide impressive results, there are numerous scenarios where these systems can stumble, leading to frustratin...
Join discussionSep 3, 2025 · 1 min read · Retrieval Augmented Generator (RAG) is seen as a way to fix the hallucination problem in large language models (LLMs). By combining an LLM with a system that finds relevant documents from outside sources, RAG bases its answers on real data and gives ...
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Aug 30, 2025 · 5 min read · Retrieval Augmented Generation (RAG) systems promise to ground AI outputs in factual, up-to-date information. However, production RAG deployments often encounter persistent failure patterns that degrade accuracy, user trust, and system reliability. U...
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Aug 30, 2025 · 3 min read · Before diving into where RAG fails, let’s first understand what it is, why it’s important, and why there’s so much hype around it. RAG stands for Retrieval-Augmented Generation. In simple terms, it combines large language models (LLMs) with a retriev...
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Aug 22, 2025 · 9 min read · Figure: A typical Retrieval-Augmented Generation (RAG) pipeline. The user’s query (1) is used to search for relevant documents (2) in the knowledge base. Those top documents are fetched as enhanced context (3–4) and fed with the original prompt to an...
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