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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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📚 GetMyEBook-Web: Making Digital Reading More Accessible

Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 🦥

📚 GetMyEBook-Web: Making Digital Reading More Accessible

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4 min read
Your AI's knowledge base had 61 pages. 24 of them described a company that no longer exists.

Your AI's knowledge base had 61 pages. 24 of them described a company that no longer exists.

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8 min read
Catalog Matching With LLMs: Why Changing Knowledge Should Stay Outside the Model

Catalog Matching With LLMs: Why Changing Knowledge Should Stay Outside the Model

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8 min read
How to Build a Lightning-Fast, 100% Private Local RAG Chatbot with Ollama, LangChain, and Streamlit

How to Build a Lightning-Fast, 100% Private Local RAG Chatbot with Ollama, LangChain, and Streamlit

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2 min read
Website to Vector Database: A Small, Traceable Pipeline

Website to Vector Database: A Small, Traceable Pipeline

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3 min read
How to Design Multi-Tenant RAG Retrieval Architecture for SaaS Help Centers — Go

How to Design Multi-Tenant RAG Retrieval Architecture for SaaS Help Centers — Go

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6 min read
What I Learned Building a RAG Chatbot From Scratch: 3 Lessons

What I Learned Building a RAG Chatbot From Scratch: 3 Lessons

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2 min read
LLM - Data Validation with Pydantic

LLM - Data Validation with Pydantic

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2 min read
Structure-Aware Chunking for Web-Crawled RAG Content

Structure-Aware Chunking for Web-Crawled RAG Content

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3 min read
How to Design Multi-Tenant Help Center Retrieval Architecture: Freshness and Chunking

How to Design Multi-Tenant Help Center Retrieval Architecture: Freshness and Chunking

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5 min read
How to Queue 100-Page Node.js Uploads (Without Blocking Support Search)

How to Queue 100-Page Node.js Uploads (Without Blocking Support Search)

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8 min read
Media Archive Rerank Guide: Fresh Chunks Before Sending to Models

Media Archive Rerank Guide: Fresh Chunks Before Sending to Models

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7 min read
Evaluate RAG Retrieval Quality: Offline Recall vs Clicks (Why Labels Win)

Evaluate RAG Retrieval Quality: Offline Recall vs Clicks (Why Labels Win)

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6 min read
I build Apify Actors for RAG pipelines — before/after: one Actor turns 41K of HTML soup into 3.6K of clean Markdown

I build Apify Actors for RAG pipelines — before/after: one Actor turns 41K of HTML soup into 3.6K of clean Markdown

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2 min read
Can Retrieval Tell Which Source to Trust?

Can Retrieval Tell Which Source to Trust?

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12 min read
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