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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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🎉 Completed AWS Generative AI Applications Specialization!

🎉 Completed AWS Generative AI Applications Specialization!

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2 min read
Intelligent RAG Optimization with GEPA: Revolutionizing Knowledge Retrieval

Intelligent RAG Optimization with GEPA: Revolutionizing Knowledge Retrieval

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6 min read
Taming the Data Tsunami: Handling Big Data in Real-Time

Taming the Data Tsunami: Handling Big Data in Real-Time

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2 min read
Cloud Cost Optimization: The Ultimate Guide to Saving You from Bill Shock

Cloud Cost Optimization: The Ultimate Guide to Saving You from Bill Shock

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2 min read
Unlocking the Power of AI: What is Prompt Engineering?

Unlocking the Power of AI: What is Prompt Engineering?

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3 min read
🧠 Comunicando o Entity Framework .NET com LLM

🧠 Comunicando o Entity Framework .NET com LLM

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4 min read
RAG-Powered Chat: OpenAI & ChromaDB Integration

RAG-Powered Chat: OpenAI & ChromaDB Integration

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5 min read
What is Context Engineering?

What is Context Engineering?

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12 min read
Spring AI: How to use Generative AI and apply RAG?

Spring AI: How to use Generative AI and apply RAG?

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10 min read
Embedded Intelligence: How SQLite-vec Delivers Fast, Local Vector Search for AI.

Embedded Intelligence: How SQLite-vec Delivers Fast, Local Vector Search for AI.

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7 min read
We built memory for AI apps focusing on individuals and achieved SOTA (88.24%) on LoCoMo benchmark

We built memory for AI apps focusing on individuals and achieved SOTA (88.24%) on LoCoMo benchmark

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2 min read
No More Forgetful Robots: My Test Drive with Cognee AI's "AI Memory"

No More Forgetful Robots: My Test Drive with Cognee AI's "AI Memory"

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3 min read
LLPY-04: Vectorización y Embeddings - Preparando Datos para RAG

LLPY-04: Vectorización y Embeddings - Preparando Datos para RAG

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13 min read
Building a RAG from Scratch: A Beginner's Guide (Part 3: Dockerization and Flexible Configuration)

Building a RAG from Scratch: A Beginner's Guide (Part 3: Dockerization and Flexible Configuration)

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3 min read
Building a RAG from Scratch: A Beginner's Guide (Part 2: Building a Web API)

Building a RAG from Scratch: A Beginner's Guide (Part 2: Building a Web API)

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2 min read
Building a 'Chat with Your Logs' System on AWS Using OpenSearch Serverless and Bedrock

Building a 'Chat with Your Logs' System on AWS Using OpenSearch Serverless and Bedrock

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7 min read
RAG Firewall: The missing retrieval-time security layer for LLMs (v0.4.1)

RAG Firewall: The missing retrieval-time security layer for LLMs (v0.4.1)

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2 min read
Building a RAG from Scratch: A Beginner's Guide (Part 1: The Basic Pipeline)

Building a RAG from Scratch: A Beginner's Guide (Part 1: The Basic Pipeline)

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3 min read
Why Agents, Not Just LLMs?

Why Agents, Not Just LLMs?

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2 min read
Building a Local Documentation Chatbot with Python, FAISS, and OpenAI

Building a Local Documentation Chatbot with Python, FAISS, and OpenAI

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7 min read
Beyond the Diff: How Deep Context Analysis Caught a Critical Bug in a 20K-Star Open Source Project

Beyond the Diff: How Deep Context Analysis Caught a Critical Bug in a 20K-Star Open Source Project

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7 min read
From Knowledge Graph Generation to RAG for Stablecoin Regulatory Intelligence

From Knowledge Graph Generation to RAG for Stablecoin Regulatory Intelligence

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11 min read
Building an Intelligent RAG Agent with Azure AI Foundry: A Deep Dive into Sreeni-RAG

Building an Intelligent RAG Agent with Azure AI Foundry: A Deep Dive into Sreeni-RAG

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8 min read
Spring AI: An Engineer’s Answer to the HR Black Hole

Spring AI: An Engineer’s Answer to the HR Black Hole

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13 min read
Supercharge Your Terminal: ShellGPT + ChromaDB + LangChain for Context-Aware Automation

Supercharge Your Terminal: ShellGPT + ChromaDB + LangChain for Context-Aware Automation

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9 min read
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