@vaidikjais
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Aug 22, 2025 · 4 min read · Introduction Retrieval-Augmented Generation (RAG) has rapidly evolved from a clever workaround for LLM hallucinations into a serious architectural pattern for enterprise AI. At its simplest, RAG retrieves relevant documents and injects them into a la...
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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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Aug 22, 2025 · 4 min read · Large Language Models (LLMs) like ChatGPT, Claude, or Gemini are powerful, but they come with a major limitation: their knowledge is frozen at the point of training. Ask a model trained in 2023 about a 2025 news headline, and it might hallucinate, ma...
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Aug 18, 2025 · 3 min read · For years, artificial intelligence has largely been reactive. You ask a question, it answers. You give a command, it obeys. Useful, yes—but limited. It’s like conversing with someone who never takes initiative, never plans ahead, never surprises you ...
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Aug 14, 2025 · 4 min read · Imagine you’re watching a magician. He pulls a card from his pocket, waves his hand, and - voilà - the trick is done. It’s dazzling, but you didn’t see the steps. The flourish hides the process. Large language models (LLMs) can feel a lot like that m...
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