Category: Machine Learning
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✍️ 𝐁𝐲 𝐀𝐛𝐡𝐢𝐬𝐡𝐞𝐤 𝐊𝐮𝐦𝐚𝐫 | #𝐅𝐢𝐫𝐬𝐭𝐂𝐫𝐚𝐳𝐲𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 ⭐ Why Every Enterprise Needs RAG Today Modern enterprises generate massive unstructured data: Most of this data remains locked, making organizations slow, dependent on SMEs, error-prone, and high-cost. A Retrieval-Augmented Generation (RAG) system solves this by: Azure provides the most robust platform to build secure, compliant, scalable enterprise RAG.…
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✍️ 𝐁𝐲 𝐀𝐛𝐡𝐢𝐬𝐡𝐞𝐤 𝐊𝐮𝐦𝐚𝐫 | #𝐅𝐢𝐫𝐬𝐭𝐂𝐫𝐚𝐳𝐲𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 Clear Differences, Use Cases, and When to Choose What 🧠 1. What is an Enterprise Agentic System? An Agentic System is an AI setup where the LLM behaves like an intelligent worker (an “agent”) that can: It focuses on autonomy and orchestration of tasks using LLM intelligence. Enterprise Agentic…
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✍️ 𝐁𝐲 𝐀𝐛𝐡𝐢𝐬𝐡𝐞𝐤 𝐊𝐮𝐦𝐚𝐫 | #FirstCrazyDeveloper 💡 Why This Matters More Than Ever Traditional keyword search engines match exact text terms but often fail to capture semantic meaning. For example, searching for “legal agreement termination” should also surface results mentioning “contract cancellation”, even if the exact words differ. This is where Vector Search shines. By…
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✍️ By Abhishek Kumar | #FirstCrazyDeveloper In today’s world of intelligent automation and AI-driven operations, event-driven agentic systems are becoming the architectural backbone for enterprises integrating AI, microservices, and human workflows. These systems are not just reactive—they are autonomous, adaptive, and context-aware, capable of executing tasks intelligently across diverse environments. This blog explores the transformative…
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✍️ By Abhishek Kumar | #FirstCrazyDeveloper Artificial Intelligence has rapidly evolved over the last decade, and Large Language Models (LLMs) have become the core drivers of this revolution. From OpenAI’s GPT family to Meta’s LLaMA, Anthropic’s Claude, Google’s Gemini, and more — each ecosystem brings unique capabilities tailored for developers, researchers, and enterprises. In this…
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✍️ by Abhishek Kumar | #FirstCrazyDeveloper “LLMs talk. RAGs talk smarter. AI Agents act. Agentic AI collaborates.” Generative AI is no longer just about writing poems or drafting emails. It’s evolving into intelligent systems that plan, reason, take actions, and even collaborate to solve real-world problems. This blog is your step-by-step guide to understanding the…
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by Abhishek Kumar | FirstCrazyDeveloper Artificial Intelligence is moving beyond static models. We are now in the Agentic AI era, where systems are not just predicting text or images but acting as autonomous problem solvers. These AI agents combine reasoning, planning, memory, and adaptability to tackle complex, real-world workflows. But what makes these agents truly agentic?The…
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by Abhishek Kumar | FirstCrazyDeveloper Building an AI Agent is not just about coding a model—it’s about creating an intelligent system that is purposeful, ethical, and continuously improving. To achieve this, the development process must follow a structured path that blends business strategy, technical design, testing rigor, and user-centered feedback loops. Let’s break down the six…
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by Abhishek Kumar | FirstCrazyDeveloper AI isn’t a one-size-fits-all solution. Different types of AI systems operate at different levels of intelligence and autonomy. Some are great at sparking creativity, some act as diligent project coordinators, and some go even further — managing the entire workflow on their own. Let’s break it down with a familiar scenario:…
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by Abhishek Kumar | FirstCrazyDeveloper In the rapidly evolving world of Artificial Intelligence, memory plays a crucial role in making AI agents more human-like, context-aware, and efficient. Just like humans, AI systems rely on different types of memory to process, store, and recall information. Each memory type serves a unique purpose, enabling AI to perform anything…
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