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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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SuperOptiX Memory: A Practical Guide for Building Agents That Remember
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SuperOptiX Memory: A Practical Guide for Building Agents That Remember

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6 min read
From Markdown to Meaning: Turn Your Obsidian Notes into a Conversational Database Using LangChain, Python, and ChromaDB
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From Markdown to Meaning: Turn Your Obsidian Notes into a Conversational Database Using LangChain, Python, and ChromaDB

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13 min read
GenAI Foundations – Chapter 5: Project Planning with the Generative AI Canvas
Cover image for GenAI Foundations – Chapter 5: Project Planning with the Generative AI Canvas

GenAI Foundations – Chapter 5: Project Planning with the Generative AI Canvas

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4 min read
GenAI Foundations – Chapter 4: Model Customization & Evaluation – Can We Trust the Outputs?
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GenAI Foundations – Chapter 4: Model Customization & Evaluation – Can We Trust the Outputs?

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9 min read
GenAI Foundations – Chapter 1: Prompt Basics: From Theory to Practice
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GenAI Foundations – Chapter 1: Prompt Basics: From Theory to Practice

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7 min read
🤯 Why Your RAG System with FAISS Is Still Failing — and How to Actually Fix It

🤯 Why Your RAG System with FAISS Is Still Failing — and How to Actually Fix It

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3 min read
Building NeuroStash - V

Building NeuroStash - V

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4 min read
🐍 How I Built a Terminal Knowledge Crawler in Pure Python (No Frameworks)
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🐍 How I Built a Terminal Knowledge Crawler in Pure Python (No Frameworks)

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4 min read
SuperOptiX: A Deep Technical Dive into the Next-Generation AI Agent Framework
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SuperOptiX: A Deep Technical Dive into the Next-Generation AI Agent Framework

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10 min read
📣 Just announced: IBM Granite-Docling: End-to-end document understanding with one tiny model

📣 Just announced: IBM Granite-Docling: End-to-end document understanding with one tiny model

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12 min read
The Future of Document Scanning: A Look at LLM-Powered OCR

The Future of Document Scanning: A Look at LLM-Powered OCR

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12 min read
HelixDB: The Blazing-Fast Graph-Vector Database You Need to Check Out!

HelixDB: The Blazing-Fast Graph-Vector Database You Need to Check Out!

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2 min read
Couchbase Weekly Updates - September 5, 2025
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Couchbase Weekly Updates - September 5, 2025

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3 min read
Vector Database: Core Concepts
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Vector Database: Core Concepts

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4 min read
Setting up RAG Locally with Ollama: A Beginner-Friendly Guide
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Setting up RAG Locally with Ollama: A Beginner-Friendly Guide

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5 min read
🤖 RAG on AWS: Building an AI-powered Knowledge Base, with Amazon Bedrock and S3 Vectors
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🤖 RAG on AWS: Building an AI-powered Knowledge Base, with Amazon Bedrock and S3 Vectors

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5 min read
GitHub Stars program

GitHub Stars program

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3 min read
The Nikki Haflinger Project: A Deep Analysis of AI Identity Transfer with Commentary from Another AI
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The Nikki Haflinger Project: A Deep Analysis of AI Identity Transfer with Commentary from Another AI

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12 min read
Building NeuroStash - III

Building NeuroStash - III

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5 min read
Repocks: Enable RAG from In-Repo Documentation
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Repocks: Enable RAG from In-Repo Documentation

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3 min read
Retrieval Technique Series-6.A Discourse on Design in High-Performance Retrieval Systems

Retrieval Technique Series-6.A Discourse on Design in High-Performance Retrieval Systems

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4 min read
🧠 Build Your Own Document Q&A Assistant with GPT, Redis & Docker
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🧠 Build Your Own Document Q&A Assistant with GPT, Redis & Docker

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3 min read
Building Neurostash - I

Building Neurostash - I

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5 min read
How To Use LLMs: Retrieval-Augmented Generation (RAG Systems)

How To Use LLMs: Retrieval-Augmented Generation (RAG Systems)

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5 min read
Automation & Optimization of Grocery Shopping

Automation & Optimization of Grocery Shopping

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1 min read
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