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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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Reverse engineering Perplexity AI: prompt injection tricks to reveal its system prompts and speed secrets

Reverse engineering Perplexity AI: prompt injection tricks to reveal its system prompts and speed secrets

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4 min read
Why Retrieval-Augmented Generation (RAG) is the Secret Weapon for Smarter Applications?

Why Retrieval-Augmented Generation (RAG) is the Secret Weapon for Smarter Applications?

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2 min read
Let’s Build A Simple ā€˜Real Estate’ Business Using AI Agents

Let’s Build A Simple ā€˜Real Estate’ Business Using AI Agents

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2 min read
Empowering AI: The QuickJS Package for LLM Tool Calling

Empowering AI: The QuickJS Package for LLM Tool Calling

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3 min read
The AI That Knows Everything (Except What You Need)

The AI That Knows Everything (Except What You Need)

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4 min read
Let’s Build Market Analysis Team with AI Agents

Let’s Build Market Analysis Team with AI Agents

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2 min read
Why Vector Databases Matter in AI: Decrypting $350 Million in Funding

Why Vector Databases Matter in AI: Decrypting $350 Million in Funding

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3 min read
Unlocking the Power of Retrieval-Augmented Generation (RAG) as Learning Tools

Unlocking the Power of Retrieval-Augmented Generation (RAG) as Learning Tools

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5 min read
The Future of Information Retrieval: RAG Models vs. Generalized AI

The Future of Information Retrieval: RAG Models vs. Generalized AI

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3 min read
Optimizing RAG Through an Evaluation-Based Methodology

Optimizing RAG Through an Evaluation-Based Methodology

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14 min read
Mapping LLM Integration Levels to Levels of Autonomous Driving, it does not have to be all or nothing!

Mapping LLM Integration Levels to Levels of Autonomous Driving, it does not have to be all or nothing!

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5 min read
Build Your Own RAG App: A Step-by-Step Guide to Setup LLM locally using Ollama, Python, and ChromaDB

Build Your Own RAG App: A Step-by-Step Guide to Setup LLM locally using Ollama, Python, and ChromaDB

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10 min read
Generating a result with a context

Generating a result with a context

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2 min read
Setting up the database and search for RAG

Setting up the database and search for RAG

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3 min read
Learning how to make an OLIVER

Learning how to make an OLIVER

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3 min read
Build Your Own GitHub Copilot with SuperDuperDB: Live Workshop šŸš€

Build Your Own GitHub Copilot with SuperDuperDB: Live Workshop šŸš€

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1 min read
How Effective are Retrieval Augmented Generation(RAG) Models?

How Effective are Retrieval Augmented Generation(RAG) Models?

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5 min read
How even the simplest RAG can empower your team

How even the simplest RAG can empower your team

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10 min read
A RAG for Elixir

A RAG for Elixir

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9 min read
The Hardest Problem in RAG... Handling 'NOT FOUND' Answers šŸ”šŸ¤”

The Hardest Problem in RAG... Handling 'NOT FOUND' Answers šŸ”šŸ¤”

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7 min read
Laravel RAG System in 4 Steps! 21:38

Laravel RAG System in 4 Steps!

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1 min read
Laravel RAG System in 4 Steps!

Laravel RAG System in 4 Steps!

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7 min read
Building a Chatbot using your documents with LangChain from Scratch

Building a Chatbot using your documents with LangChain from Scratch

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9 min read
Protecting RAG Application Against Prompt Injection

Protecting RAG Application Against Prompt Injection

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5 min read
How RAG with txtai works

How RAG with txtai works

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8 min read
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