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# rag

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data.

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Beyond Vector Search: Building a RAG That *Actually* Understands Your Data

Beyond Vector Search: Building a RAG That *Actually* Understands Your Data

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7 min read
Building a RAG from Scratch: A Beginner's Guide (Part 2: Building a Web API)
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Building a RAG from Scratch: A Beginner's Guide (Part 2: Building a Web API)

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2 min read
Building a 'Chat with Your Logs' System on AWS Using OpenSearch Serverless and Bedrock

Building a 'Chat with Your Logs' System on AWS Using OpenSearch Serverless and Bedrock

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7 min read
RAG Firewall: The missing retrieval-time security layer for LLMs (v0.4.1)
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RAG Firewall: The missing retrieval-time security layer for LLMs (v0.4.1)

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2 min read
Building a RAG from Scratch: A Beginner's Guide (Part 1: The Basic Pipeline)
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Building a RAG from Scratch: A Beginner's Guide (Part 1: The Basic Pipeline)

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3 min read
Why Agents, Not Just LLMs?
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Why Agents, Not Just LLMs?

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2 min read
Building a Local Documentation Chatbot with Python, FAISS, and OpenAI
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Building a Local Documentation Chatbot with Python, FAISS, and OpenAI

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7 min read
🎥 Model Context Protocol (MCP) Clearly Explained in Hindi

🎥 Model Context Protocol (MCP) Clearly Explained in Hindi

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1 min read
From Knowledge Graph Generation to RAG for Stablecoin Regulatory Intelligence
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From Knowledge Graph Generation to RAG for Stablecoin Regulatory Intelligence

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11 min read
Quick Framework and some Performance Improvements
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Quick Framework and some Performance Improvements

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6 min read
Building an Intelligent RAG Agent with Azure AI Foundry: A Deep Dive into Sreeni-RAG
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Building an Intelligent RAG Agent with Azure AI Foundry: A Deep Dive into Sreeni-RAG

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8 min read
Spring AI: An Engineer’s Answer to the HR Black Hole
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Spring AI: An Engineer’s Answer to the HR Black Hole

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13 min read
Supercharge Your Terminal: ShellGPT + ChromaDB + LangChain for Context-Aware Automation
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Supercharge Your Terminal: ShellGPT + ChromaDB + LangChain for Context-Aware Automation

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9 min read
From LLMs to Liability: How Agents Grow Up

From LLMs to Liability: How Agents Grow Up

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1 min read
OrKa 0.9.4: cleaner logs, full GraphScout paths, ISO timestamps

OrKa 0.9.4: cleaner logs, full GraphScout paths, ISO timestamps

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1 min read
Context Engineering: The Missing Piece in Building AI Agents That Actually Work
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Context Engineering: The Missing Piece in Building AI Agents That Actually Work

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4 min read
I Created an AI Assistant That Reads the Fine Print for You

I Created an AI Assistant That Reads the Fine Print for You

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4 min read
**Processing Mode**

**Processing Mode**

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3 min read
RAG for Dummies
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RAG for Dummies

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2 min read
AI-Powered Resume & Job Description Matching with RAG
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AI-Powered Resume & Job Description Matching with RAG

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1 min read
Vector Databases Guide: RAG Applications 2025
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Vector Databases Guide: RAG Applications 2025

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10 min read
The Secret to Efficient RAG: A Step-by-Step Guide to Chunking and Counting Your Vectors

The Secret to Efficient RAG: A Step-by-Step Guide to Chunking and Counting Your Vectors

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11 min read
LLPY-03: Extracción y Procesamiento Inteligente de Datos Legales
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LLPY-03: Extracción y Procesamiento Inteligente de Datos Legales

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21 min read
RAG: experiments with prompting using 3 LLM's
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RAG: experiments with prompting using 3 LLM's

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8 min read
Embedded Intelligence: How SQLite-vec Delivers Fast, Local Vector Search for AI.

Embedded Intelligence: How SQLite-vec Delivers Fast, Local Vector Search for AI.

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7 min read
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