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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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Does anyone know what will be sent to LLM when I use Github copilot?

Does anyone know what will be sent to LLM when I use Github copilot?

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1 min read
From Brittle to Brilliant: A Developer's Guide to Building Trustworthy Graph RAG with Local LLMs

From Brittle to Brilliant: A Developer's Guide to Building Trustworthy Graph RAG with Local LLMs

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3 min read
🧠 GenAI as a Backend Engineer: Part 3 — RAG with LlamaIndex

🧠 GenAI as a Backend Engineer: Part 3 — RAG with LlamaIndex

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4 min read
Building a RAG System with Vertex AI, Pinecone, and LangChain (Step-by-Step Guide)

Building a RAG System with Vertex AI, Pinecone, and LangChain (Step-by-Step Guide)

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6 min read
Lessons & Practices for Building and Optimizing Multi-Agent RAG Systems with DSPy and GEPA

Lessons & Practices for Building and Optimizing Multi-Agent RAG Systems with DSPy and GEPA

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6 min read
Building NeuroStash - VI

Building NeuroStash - VI

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3 min read
Building with Generative AI: Lessons from 5 Projects Part 1: RAG

Building with Generative AI: Lessons from 5 Projects Part 1: RAG

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8 min read
RAG-Powered Chat: OpenAI & ChromaDB Integration

RAG-Powered Chat: OpenAI & ChromaDB Integration

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5 min read
How the Pool Pattern Works in Multi-tenant RAG

How the Pool Pattern Works in Multi-tenant RAG

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2 min read
SuperOptiX Memory: A Practical Guide for Building Agents That Remember

SuperOptiX Memory: A Practical Guide for Building Agents That Remember

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6 min read
AWS User Group Chennai Meetup - Session 3: A Serverless AI-Powered e-Learning Assistant

AWS User Group Chennai Meetup - Session 3: A Serverless AI-Powered e-Learning Assistant

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

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

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

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

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

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

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

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9 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
We built memory for AI apps focusing on individuals and achieved SOTA (88.24%) on LoCoMo benchmark

We built memory for AI apps focusing on individuals and achieved SOTA (88.24%) on LoCoMo benchmark

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

🐍 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

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

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10 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

Couchbase Weekly Updates - September 5, 2025

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3 min read
Rethinking LLM-Powered Apps: Ditching Tool Overload for Smarter Query Abstraction

Rethinking LLM-Powered Apps: Ditching Tool Overload for Smarter Query Abstraction

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3 min read
Vector Database: Core Concepts

Vector Database: Core Concepts

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4 min read
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