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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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Building AI Agents: A 3-Level Roadmap for Developers

Building AI Agents: A 3-Level Roadmap for Developers

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6 min read
Why ChatGPT Hallucinates UAE Visa Rules — and How I Built a Localized AI Chat That Doesnt

Why ChatGPT Hallucinates UAE Visa Rules — and How I Built a Localized AI Chat That Doesnt

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5 min read
I am new here

I am new here

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1 min read
LLM Persistent Memory & Python Tooling Elevate AI Agent Workflows

LLM Persistent Memory & Python Tooling Elevate AI Agent Workflows

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3 min read
Building Production RAG Pipelines: Practical Lessons

Building Production RAG Pipelines: Practical Lessons

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5 min read
Day 7 - Dense Embedding - RAG

Day 7 - Dense Embedding - RAG

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2 min read
CRAG Implementation using TS

CRAG Implementation using TS

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2 min read
Reel VCR for LLM APIs, AI-Generated PySpark & MacOS AI Agent Demo

Reel VCR for LLM APIs, AI-Generated PySpark & MacOS AI Agent Demo

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3 min read
Part 6 — RAG Recall Quality from 60% to 93%: Building a Continuous Evaluation Loop (Not Gut Feeling)

Part 6 — RAG Recall Quality from 60% to 93%: Building a Continuous Evaluation Loop (Not Gut Feeling)

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10 min read
Search in the LLM Era: Vector RAG vs Vectorless Search vs LLM-Wiki

Search in the LLM Era: Vector RAG vs Vectorless Search vs LLM-Wiki

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4 min read
RAG in production: three levels from search to conversation

RAG in production: three levels from search to conversation

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3 min read
Tigergraph Hackathon

Tigergraph Hackathon

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4 min read
Day 13 of 60: Today I Started Learning RAG

Day 13 of 60: Today I Started Learning RAG

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2 min read
How to Build a HIPAA Compliant AI Ecosystem Without the Cloud

How to Build a HIPAA Compliant AI Ecosystem Without the Cloud

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15 min read
RAG SOTA: I Tested 7 Pipelines and Built SEQUOIA (Open Source)

RAG SOTA: I Tested 7 Pipelines and Built SEQUOIA (Open Source)

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2 min read
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