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RAG, or Retrieval-Augmented Generation, is a technique used in natural language processing (NLP) and machine learning.

In this talk, we explore how Language Model (LLM) technology coupled with Retrieval Augmented Generation (RAG) systems ...

Are you building RAG app wrong? RAG dev lifecycle is different? How to take an advantage of RAG application?

Reranker models can help improve the responses of RAG-based applications. These models offer several key benefits: 1.

Confused about RAG (Retrieval-Augmented Generation)? In this short, I break it down in the simplest way possible — under 1 ...

... and operationalizing RAG (Retrieval-Augmented Generation) pipelines using open-source Large Language Models (LLMs).

I had a blast chatting with Steven Touw, Co-Founder & CTO of Immuta on The Ravit Show. Here are some key highlights: -- Steve ...

Is Your RAG App Failing? Diagnose & Fix It with Real Metrics Are your RAG-based applications underperforming or giving ...

We will recommend you top project ideas from you experience and your interest with a simple prompt.

The ground is constantly shifting in the world of LLM research. New ideas and architectures come and go quickly. However, RAG ...

In this episode, we break down the groundbreaking research paper, "UniversalRAG: Retrieval-Augmented Generation over ...

This video introduces and dives into all major topics used in vanilla RAG and sets up background for advanced RAG topics.

Techincal data: * RAG (Retrieval-Augmented Generation) implemented using ChromaDB and SentenceTransformers.

At Imagine, I used industrial glasses to examine and gather information about a circuit board part. With the glasses, I could detect ...

It consists of two main components: a documentation generator and a RAG-based (Retrieval-Augmented Generation) query ...

GenAI is NOT Complicated — Let Me Show You! If you think Generative AI is only for techies or coders, this video is for you.

... Team Covers large language model tokenizers, attention mechanisms, retrieval-augmented generation (RAG), and fine-tuning.