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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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I Was Done Getting Answers - So I Built RAG That Asks Questions Too

I Was Done Getting Answers - So I Built RAG That Asks Questions Too

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5 min read
Why Your RAG System Hallucinations Start at Ingestion, Not the LLM

Why Your RAG System Hallucinations Start at Ingestion, Not the LLM

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3 min read
Ok RAG, but what about data extraction from documents?

Ok RAG, but what about data extraction from documents?

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1 min read
AI-ассистент для документации из wiki Яндекса с использованием RAG и LangChain
Cover image for AI-ассистент для документации из wiki Яндекса с использованием RAG и LangChain

AI-ассистент для документации из wiki Яндекса с использованием RAG и LangChain

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Comments
1 min read
Building a Vector Database from Scratch - CapybaraDB
Cover image for Building a Vector Database from Scratch - CapybaraDB

Building a Vector Database from Scratch - CapybaraDB

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11 min read
I Built a Chrome Extension to Extract YouTube Transcripts in Bulk

I Built a Chrome Extension to Extract YouTube Transcripts in Bulk

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5 min read
Reasoning In The Wild: How I Actually Think About Cognition
Cover image for Reasoning In The Wild: How I Actually Think About Cognition

Reasoning In The Wild: How I Actually Think About Cognition

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10 min read
I built ARIA - Adaptive Resonant Intelligent Architecture

I built ARIA - Adaptive Resonant Intelligent Architecture

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6 min read
How Data Formatting (Line Breaks and Indentation) Affects LLM Response Accuracy in RAG

How Data Formatting (Line Breaks and Indentation) Affects LLM Response Accuracy in RAG

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4 min read
"Project C.O.R.E : How to get started with Vector Database, RAG and LLM with an Example of Personalized Tutor"
Cover image for "Project C.O.R.E : How to get started with Vector Database, RAG and LLM with an Example of Personalized Tutor"

"Project C.O.R.E : How to get started with Vector Database, RAG and LLM with an Example of Personalized Tutor"

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1 min read
DragonMemory: Neural Sequence Compression for Production RAG

DragonMemory: Neural Sequence Compression for Production RAG

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8 min read
Building Tiramisu: An Open-Source Multi-Expert RAG Framework for Marketing Consultancy

Building Tiramisu: An Open-Source Multi-Expert RAG Framework for Marketing Consultancy

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3 min read
RAG vs MCP: Understanding AI Context Solutions
Cover image for RAG vs MCP: Understanding AI Context Solutions

RAG vs MCP: Understanding AI Context Solutions

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6 min read
🧠Deterministic scoring for messy AI agent graphs: what I learned building OrKa v0.9.6
Cover image for 🧠Deterministic scoring for messy AI agent graphs: what I learned building OrKa v0.9.6

🧠Deterministic scoring for messy AI agent graphs: what I learned building OrKa v0.9.6

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Comments 6
10 min read
I stopped talking about AI and started to build: Build a RAG System with Amazon Bedrock, Titan Embeddings & Knowledge Bases
Cover image for I stopped talking about AI and started to build: Build a RAG System with Amazon Bedrock, Titan Embeddings & Knowledge Bases

I stopped talking about AI and started to build: Build a RAG System with Amazon Bedrock, Titan Embeddings & Knowledge Bases

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8 min read
Dejé de hablar de AI y empecé a construir: Construye un RAG simple con Amazon Bedrock Knowledge Bases.
Cover image for Dejé de hablar de AI y empecé a construir: Construye un RAG simple con Amazon Bedrock Knowledge Bases.

Dejé de hablar de AI y empecé a construir: Construye un RAG simple con Amazon Bedrock Knowledge Bases.

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9 min read
How I Created Superior RAG Retrieval With 3 Files in Supabase
Cover image for How I Created Superior RAG Retrieval With 3 Files in Supabase

How I Created Superior RAG Retrieval With 3 Files in Supabase

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8 min read
When AI Predicts Too Well: Understanding Hallucinations in Large Language Models
Cover image for When AI Predicts Too Well: Understanding Hallucinations in Large Language Models

When AI Predicts Too Well: Understanding Hallucinations in Large Language Models

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4 min read
Building Syllabi – Agentic AI with Vercel AI SDK, Dynamic Tool Loading, and RAG
Cover image for Building Syllabi – Agentic AI with Vercel AI SDK, Dynamic Tool Loading, and RAG

Building Syllabi – Agentic AI with Vercel AI SDK, Dynamic Tool Loading, and RAG

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9 min read
How to Add Real-Time Web Search to Your LLM Using Tavily
Cover image for How to Add Real-Time Web Search to Your LLM Using Tavily

How to Add Real-Time Web Search to Your LLM Using Tavily

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5 min read
Setting up Linagora’s OpenRAG locally

Setting up Linagora’s OpenRAG locally

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6 min read
ChatGPT Actions, Not Just Chat

ChatGPT Actions, Not Just Chat

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2 min read
Build a AI Voice Agent Using RAG Pipeline and VideoSDK
Cover image for Build a AI Voice Agent Using RAG Pipeline and VideoSDK

Build a AI Voice Agent Using RAG Pipeline and VideoSDK

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5 min read
Why Evals and Observability Should Be an AI Builder’s Top Concern

Why Evals and Observability Should Be an AI Builder’s Top Concern

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7 min read
Medical Chatbots with RAG
Cover image for Medical Chatbots with RAG

Medical Chatbots with RAG

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6 min read
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