The magic of RAG is in the retrieval

Quick Summary:

Any leading large language model will do.

To succeed with retrieval-augmented generation, focus on optimizing the retrieval model and ensuring high-quality data.  The decades-long pursuit to capture, organize and apply the collective knowledge within an enterprise has failed time and again because available software tools were incapable of understanding the noisy unstructured data that comprises the vast majority of the enterprise knowledge base.Until now. Large language models (LLMs) that power generative AI tools excel at processing and understanding unstructured data, making them ideal for powering enterprise knowledge management systems.

Therefore, most of the development effort should focus on optimizing the retrieval model and ensuring high-quality data.

---

More Pictures

---

Read Quick SummarySave Time

or

Article Details

Author / Journalist: Chandini Jain

Category: Technology

Markets: United States

Topics: Retrieval augmented generation Article RAG Artificial Intelligence AI

Source Website Secure: Yes (HTTPS)

News Sentiment: Negative

Fact Checked: Legitimate

Article Type: News Report

Published On: 2024-09-11 @ 09:35:28 (13 hours ago)

News Timezone: GMT +8:00

News Source URL: betanews.com

Language: English

Article Length: 1112 words

Reading Time: 7 minutes read

Sentences: 52 lines

Sentence Length: 22 words per sentence (average)

Platforms: Desktop Web, Mobile Web, iOS App, Android App

Copyright Owner: © BetaNews

News ID: 22457973

View Article Analysis

About BetaNews

Main Topics: Technology

Official Website: betanews.com

Update Frequency: 7 posts per day

Year Established: 1992

Headquarters: United States

News Last Updated: 9 hours ago

Coverage Areas: United States

Ownership: Independent Company

Publication Timezone: GMT +8:00

Content Availability: Worldwide

News Language: English

RSS Feed: Available (XML)

API Access: Available (JSON, REST)

Website Security: Secure (HTTPS)

Publisher ID: #58

Publisher Details

Frequently Asked Questions