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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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STOP GUESSING: The Observability Stack I Built to Debug My Failing AI Agents

STOP GUESSING: The Observability Stack I Built to Debug My Failing AI Agents

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3 min read
Human-in-the-Loop Systems: Building AI That Knows When to Ask for Help

Human-in-the-Loop Systems: Building AI That Knows When to Ask for Help

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17 min read
Prompt -> RAG -> Eval: System Overview for LLM Engineers

Prompt -> RAG -> Eval: System Overview for LLM Engineers

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3 min read
Implementing Retrieval-Augmented Generation (RAG) with Real-World Constraints

Implementing Retrieval-Augmented Generation (RAG) with Real-World Constraints

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3 min read
Kreuzberg v4.0.0-RC.8 is Available

Kreuzberg v4.0.0-RC.8 is Available

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8 min read
Functional MCP AI System Diagram

Functional MCP AI System Diagram

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1 min read
Our RAG system still failed on hierarchical metrics — Part 2

Our RAG system still failed on hierarchical metrics — Part 2

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6 min read
Why GenAI Observability Breaks in Production

Why GenAI Observability Breaks in Production

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2 min read
Multi-Step Reasoning and Agentic Workflows: Building AI That Plans and Executes

Multi-Step Reasoning and Agentic Workflows: Building AI That Plans and Executes

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16 min read
RAG for Developers — Built for Code, Not Just Text (Review Requested)

RAG for Developers — Built for Code, Not Just Text (Review Requested)

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1 min read
RAG Chunking Strategies Deep Dive

RAG Chunking Strategies Deep Dive

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7 min read
Stop feeding garbage to your LLM: How to get clean Markdown from Documentation

Stop feeding garbage to your LLM: How to get clean Markdown from Documentation

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1 min read
My hands-on experience with Qdrant and Docling (and Ollama)

My hands-on experience with Qdrant and Docling (and Ollama)

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11 min read
Building a Simple RAG System Using FAISS

Building a Simple RAG System Using FAISS

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3 min read
Reranking and Two-Stage Retrieval: Precision When It Matters Most

Reranking and Two-Stage Retrieval: Precision When It Matters Most

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2 min read
LLMs Hallucinate. RAG Fixes That — Here’s How We Built a Reliable Healthcare AI

LLMs Hallucinate. RAG Fixes That — Here’s How We Built a Reliable Healthcare AI

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3 min read
Build Better RAG Pipelines: Scraping Technical Docs to Clean Markdown

Build Better RAG Pipelines: Scraping Technical Docs to Clean Markdown

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2 min read
I Built a TUI to Visualize RAG Chunking because chunk_size=1000 is a Lie 📉

I Built a TUI to Visualize RAG Chunking because chunk_size=1000 is a Lie 📉

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3 min read
Weaviate for RAG: When It Shines (and When It Doesn’t)

Weaviate for RAG: When It Shines (and When It Doesn’t)

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2 min read
Our RAG system failed to understand KPIs — Part 1: Metric retrieval design

Our RAG system failed to understand KPIs — Part 1: Metric retrieval design

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5 min read
Why your AI assistant lies to you (and how to fix it)

Why your AI assistant lies to you (and how to fix it)

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4 min read
Docify: Building a Production RAG System for Knowledge Management

Docify: Building a Production RAG System for Knowledge Management

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4 min read
How to Build a Scalable RAG-Based Chatbot on AWS?

How to Build a Scalable RAG-Based Chatbot on AWS?

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8 min read
CLaRa: Fixing RAG’s Broken Retrieval–Generation Pipeline With Shared-Space Learning

CLaRa: Fixing RAG’s Broken Retrieval–Generation Pipeline With Shared-Space Learning

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3 min read
A RAG-Free Technique That Makes LLM Outputs Stable, Predictable, and Auditable

A RAG-Free Technique That Makes LLM Outputs Stable, Predictable, and Auditable

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2 min read
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