Category: Prompt Engineering
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โ๏ธ ๐๐ฒ ๐๐๐ก๐ข๐ฌ๐ก๐๐ค ๐๐ฎ๐ฆ๐๐ซ | #๐ ๐ข๐ซ๐ฌ๐ญ๐๐ซ๐๐ณ๐ฒ๐๐๐ฏ๐๐ฅ๐จ๐ฉ๐๐ซ โญ 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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โ๏ธ ๐๐ฒ ๐๐๐ก๐ข๐ฌ๐ก๐๐ค ๐๐ฎ๐ฆ๐๐ซ | #๐ ๐ข๐ซ๐ฌ๐ญ๐๐ซ๐๐ณ๐ฒ๐๐๐ฏ๐๐ฅ๐จ๐ฉ๐๐ซ Clear Differences, Use Cases, and When to Choose What ๐ง 1. What is an Enterprise Agentic System? An Agentic System is an AI setup where the LLM behaves like an intelligent worker (an โagentโ) that can: It focuses on autonomy and orchestration of tasks using LLM intelligence. Enterprise Agentic…
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โ๏ธ ๐๐ฒ ๐๐๐ก๐ข๐ฌ๐ก๐๐ค ๐๐ฎ๐ฆ๐๐ซ | #๐ ๐ข๐ซ๐ฌ๐ญ๐๐ซ๐๐ณ๐ฒ๐๐๐ฏ๐๐ฅ๐จ๐ฉ๐๐ซ ๐ง Why Do We Need Vector Databases? Traditional databases store structured data (rows & columns) and perform keyword or pattern matching.But AI systems donโt think in keywords โ they think in meanings. For example: Thatโs why modern AI systems like ChatGPT, RAG pipelines, and semantic search engines rely on…
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โ๏ธ ๐๐ฒ ๐๐๐ก๐ข๐ฌ๐ก๐๐ค ๐๐ฎ๐ฆ๐๐ซ | #FirstCrazyDeveloper ๐ก 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 โlegal agreement terminationโ should also surface results mentioning โcontract cancellationโ, even if the exact words differ. This is where Vector Search shines. By…
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โ๏ธ By Abhishek Kumar | #FirstCrazyDeveloper In todayโs 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 reactiveโthey are autonomous, adaptive, and context-aware, capable of executing tasks intelligently across diverse environments. This blog explores the transformative…
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โ๏ธ by Abhishek Kumar | #FirstCrazyDeveloper โLLMs talk. RAGs talk smarter. AI Agents act. Agentic AI collaborates.โ Generative AI is no longer just about writing poems or drafting emails. Itโs 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 โ #FirstCrazyDeveloper Artificial Intelligence is moving fast. But building an AI prototype and building a production-grade AI platform are two very different challenges. In this blog, Iโll break down how developers can architect such a system using Azure with step-by-step real-world examples. ๐ 1. Cloud Infrastructure โ The Foundation Every reliable AI…
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by Abhishek Kumar |ย FirstCrazyDeveloper AI isnโt 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 โ managing the entire workflow on their own. Letโs break it down with a familiar scenario:…
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by Abhishek Kumar |ย FirstCrazyDeveloper Artificial Intelligence has become a core enabler for solving real-world challenges, but the way we adapt AI models to meet our needs can vary greatly. Three of the most common strategies today are RAG (Retrieval-Augmented Generation), Fine-Tuning, and Prompt Engineering. Each approach offers unique benefits and trade-offs depending on the use…
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