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Stop getting confused by “Model Context Protocol” (MCP). Here is the easiest way to understand it. 🧠✨ Student = LLM 🤖 Teachers = External Data Sources 📊 The Connector = MCP 🌉 MCP is simply the p
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RAG in 30 seconds. No fluff. Just clarity. ⚡ From raw data → indexing → querying → final answer Everything comes together here. If you understood this, you’ve basically unlocked how modern AI apps a
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Watch the full video on YouTube 🎥 Vectorless RAG sounds confusing… until you realize you don’t actually need embeddings to build powerful retrieval systems 🤯 In this reel, I’ve broken down the ide
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Watch the full video on YouTube 🎥 Check the bio for the link ! Vectorless RAG sounds confusing… until you realize you don’t actually need embeddings to build powerful retrieval systems 🤯 In this r
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Imagine you ask: 👉 “Find the latest sales report and email it to my manager” Here’s what happens behind the scenes 👇 1️⃣ AI realizes: “I can’t do this alone” 2️⃣ Finds the right tools (database +
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🚀 MCP isn’t complicated… you’re just missing the 3 pieces 👇 🧠 MCP Host → The brain (makes decisions) 🔌 MCP Client → The connector (handles communication) ⚙️ MCP Server → The executor (does the ac
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Your data isn’t searched by keywords… it’s searched by meaning. 🧠 That’s where Vector DB comes in. Instead of storing text, it stores embeddings — numerical representations of meaning. So when you
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Indexing stores the knowledge… but this is where the magic happens. ⚡ In the Query Phase, your question actually meets the data. User query → search in vector DB → retrieve relevant chunks → send to
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RAG isn’t just magic… there’s a process behind it. 🧠 In this part, I break down the Indexing Phase — the step where your data actually becomes usable for LLMs. From raw data → chunking → embeddings
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Everyone talks about RAG… but this is where it actually starts 👇 Part 3B🚀 How to give any data as context to an LLM (using a simple example) Start simple before going advanced. #artificialintelli
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Everyone talks about RAG… but this is where it actually starts 👇 Part 2 🚀 How to give any data as context to an LLM (using a simple example) Start simple before going advanced. #artificialintelli
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Everyone talks about RAG… but this is where it actually starts 👇 Part 2 🚀 How to give data as context to an LLM (the traditional way) Start simple before going advanced. #ai #artificialintellige
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About @pratham.learns.tech

Pratham is a Instagram nano influencer. With 1.6K followers and 16 posts, pratham.learns.tech's account shows a follower-to-following ratio of N/A:1. View all content anonymously on Pictame - no login required.

pratham.learns.tech's engagement rate sits at 13.55%. That's 171% above what you'd typically see for accounts with 1K-10K followers (average 5%) — a sign of a genuinely connected audience.

Out of the last 12 posts, 100% are videos and 0% are photos. New posts appear roughly every 2 days — a steady, reliable rhythm. Captions lean moderate, averaging 435 characters.

13.55% engagement rate — 171% above average for 1K-10K follower accounts.
New content every 2 days — a steady rhythm.

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@pratham.learns.tech

Pratham

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#ai#artificialintelligence#learning#tech#education#learnwithme#rag#mcp

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Computer Engineer exploring modern tech 🚀 Explore | Learn | Share Documenting what I learn 📕 pratham.learns.tech@gmai...

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