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Looking for GenAI internship/full-time roles! At Zomato, I built GPT-4o ticket systems, LLM eval pipelines & OCR tools (cut ops time 85%). For fun personal projects, Fine-tuned Mistral 7B for persona chat, built ANPR & haptic gloves too Skilled in P

Get RAG-ready data from any unstructured document. This is crazy for AI companies. I've explained below. 𝗦𝘁𝗲𝗽 1 – 𝗨𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝘀 (𝗧𝗵𝗲 𝗦𝗼𝘂𝗿𝗰𝗲) • Real-world PDFs and documents are messy. Tables, images, signa

🚨 Everyone says "RAG is dead" — but I say: It’s just been badly implemented. I’ve worked on AI systems where Retrieval-Augmented Generation (RAG) either changed the game… or completely flopped. Here’s the hard truth 👇 --- 🤯 Most teams mess up

Here's a short breakdown of Perplexity's "AI Super Agent" technicalities: 1. Advanced LLM Core: Uses a blend of powerful LLMs (e.g., GPT-4o, Claude 3.5, Sonar) for reasoning. 2. Autonomous Workflows: Breaks down complex tasks into self-executing, m

🚨 BREAKING: Anthropic just dropped the tutorial on "Building AI Agents with Claude Agent SDK" Here's what it covers: > Agent Loop Gather context → Take action → Verify work → Repeat. Your agent searches files, executes tasks, checks its output, t

If you're building AI agents, you should get familiar with these 3 common agent/workflow patterns. Let's break it down. 🔹 Reflection You give the agent an input. The agent then "reflects" on its output, and based on feedback, improves and refines.

If you're building anything in AI, these matter a lot. I've explained in simple below. 1. 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰𝘀 Create structured meaning so AI understands context clearly. Instead of raw text, information is organized with relationships and concepts.

5 Layers of AI Product Development. I've explained each in short, simple steps below. 1. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 → Handles everything the user sees and interacts with → Ensures login security and smooth user experience → Stores session co

6 Chunking Methods for RAG you should know. I’ve explained it in a simple, step by step way. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴? 1. Chunking means splitting large documents into smaller pieces. 2. Helps LLMs search and understand data better. 3. Essent