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How to Build a Useful RAG Application?

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Published Sep 18 '24. Last edited Oct 19 '24

Uncertainty

This topic is a collection of posts regarding how to build a useful Retrieval Augmented Generation (RAG) application.

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Oct 16 '24
Fact

My Notes of Test Using Google NotebookLM
10/16 Google NotebookLM is a notes taking app built with Gemini 1.5 Pro behind it. Gemini 1.5 Pro is 20 times more expensive than Gemini 1.0 Pro, according to source Language Supported: Notebook...

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Sep 29 '24
Opinion

NotebookLM’s automatically generated podcasts are surprisingly effective
NotebookLM is effectively an end-user customizable RAG product. It lets you gather together multiple “sources”—documents, pasted text, links to web pages and YouTube videos—into a single interfa...

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Sep 26 '24
Fact

Google's NotebookLM can now transform YouTube videos into study guides - here's how | ZDNET

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Sep 26 '24
Fact

A Guide to Improving Long Context Instruction Following on Open Source Models
Here is a summary of the article in 10 bullet points: Long context models are gaining traction in the field of large language models (LLMs) due to their ability to process extensive inputs, pot...

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Sep 18 '24
Prediction

how to measure RAG accuracy? : r/Rag
It looks like one needs to have a ground truth table to evaluate the performance, but what about if you are not an expert from the field and you wanna know how trustable the answer is in some nu...

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How to Build a Useful RAG Application?

My Notes of Test Using Google NotebookLM

NotebookLM’s automatically generated podcasts are surprisingly effective

Google's NotebookLM can now transform YouTube videos into study guides - here's how | ZDNET

A Guide to Improving Long Context Instruction Following on Open Source Models

how to measure RAG accuracy? : r/Rag