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AI, Backend Engineering & Architecture Guides

  • Breaking Big Problems into Small, Reliable Steps with Prompt Chaining

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    Large Language Models (LLMs) often feel intelligent enough to handle anything in a single instruction. But in real world applications, giving one massive prompt is like asking a human to cook, serve, clean, and manage accounts at the same time quality drops quickly. Prompt chaining treats an LLM like a team member in a…

  • There is nothing Random inside a computer

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    Have you ever wondered how your music app shuffles thousands of songs, or how a video game generates a brand new world every time you hit “New Game”? It feels like digital magic. We call it “random,” but in the world of silicon and circuits, true randomness is actually quite hard to find. In…

  • Django Forms & Validations — From Simple to Advanced

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    When I first started learning Django, forms looked magical. I would write a few lines, call is_valid(), and Django somehow knew whether my data was correct or not. Only later did I realize how much work Django does behind the scenes (batteries included) type conversion, sanitization, security checks, and structured validation. Forms are the…

  • MovieLens 360: User Retention, Recommendation & Engagement Analytics

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    Imagine You are working as a Data Scientist in “StreamFlix” – a Netflix-like platform Management Problems DATA SOURCE MovieLens Dataset (Core) https://grouplens.org/datasets/movielens/25m/ Tables: BUSINESS OBJECTIVES You must build PROBLEM STATEMENT FOR YOU You must act as a Data Scientist and deliver: A. Analytics Layer B. ML Layer C. Engineering Layer DETAILED EXPECTATIONS PHASE 1…

  • E-Commerce Customer 360, Churn & Revenue Analytics

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    1. Business Problem (Industry Scenario) You are hired as a Data Scientist for an online marketplace similar to Amazon/Flipkart. The company has 3 major issues Management Questions 2. Data Source (REAL) We will use Brazilian Olist E-commerce Datasethttps://www.kaggle.com/datasets/olistbr/brazilian-ecommerce Sample Database Look, Table Purpose customers user info orders purchase header order_items product level payments payment…