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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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Why GenAI Observability Breaks in Production

Why GenAI Observability Breaks in Production

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2 min read
Launching your personal assistant

Launching your personal assistant

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14 min read
Why RAG is the Future of Search (And How Elastic Search Makes it Possible )

Why RAG is the Future of Search (And How Elastic Search Makes it Possible )

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4 min read
Before You Build a Client RAG/Agent: My Pre-Build Checklist (With Examples + What to Automate)

Before You Build a Client RAG/Agent: My Pre-Build Checklist (With Examples + What to Automate)

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5 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
I made a fast, structured PDF extractor for RAG; 300 pages a second

I made a fast, structured PDF extractor for RAG; 300 pages a second

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3 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
Why Static Load Balancing Fails for LLM Infrastructure (And What Works Instead)

Why Static Load Balancing Fails for LLM Infrastructure (And What Works Instead)

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7 min read
Beyond RAG: Building an Autonomous "Epistemic Engine" to Fight AI Hallucination

Beyond RAG: Building an Autonomous "Epistemic Engine" to Fight AI Hallucination

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2 min read
Building a RAG-Powered Documentation Assistant: Why I Used Bifrost LLM Gateway Instead of Direct API Calls

Building a RAG-Powered Documentation Assistant: Why I Used Bifrost LLM Gateway Instead of Direct API Calls

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5 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
RAG-Augmented Agile Story Generation: An Architectural Framework for LLM-Powered Backlog Automation

RAG-Augmented Agile Story Generation: An Architectural Framework for LLM-Powered Backlog Automation

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8 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
Multi-Tenant Design for Bedrock Knowledge Base: Solving the Account Limit with Metadata Filtering

Multi-Tenant Design for Bedrock Knowledge Base: Solving the Account Limit with Metadata Filtering

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3 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
Create a Knowledge Base in Amazon Bedrock (Step-by-Step Console Guide)

Create a Knowledge Base in Amazon Bedrock (Step-by-Step Console Guide)

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6 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
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
Course: Large Language Models and Generative AI for NLP — 2025

Course: Large Language Models and Generative AI for NLP — 2025

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1 min read
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