Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Read by thought-leaders and decision-makers around the world. Phone Number: +1-650-246-9381 Email: [email protected]
228 Park Avenue South New York, NY 10003 United States
Website: Publisher: https://towardsai.net/#publisher Diversity Policy: https://towardsai.net/about Ethics Policy: https://towardsai.net/about Masthead: https://towardsai.net/about
Name: Towards AI Legal Name: Towards AI, Inc. Description: Towards AI is the world's leading artificial intelligence (AI) and technology publication. Founders: Roberto Iriondo, , Job Title: Co-founder and Advisor Works for: Towards AI, Inc. Follow Roberto: X, LinkedIn, GitHub, Google Scholar, Towards AI Profile, Medium, ML@CMU, FreeCodeCamp, Crunchbase, Bloomberg, Roberto Iriondo, Generative AI Lab, Generative AI Lab Denis Piffaretti, Job Title: Co-founder Works for: Towards AI, Inc. Louie Peters, Job Title: Co-founder Works for: Towards AI, Inc. Louis-François Bouchard, Job Title: Co-founder Works for: Towards AI, Inc. Cover:
Towards AI Cover
Logo:
Towards AI Logo
Areas Served: Worldwide Alternate Name: Towards AI, Inc. Alternate Name: Towards AI Co. Alternate Name: towards ai Alternate Name: towardsai Alternate Name: towards.ai Alternate Name: tai Alternate Name: toward ai Alternate Name: toward.ai Alternate Name: Towards AI, Inc. Alternate Name: towardsai.net Alternate Name: pub.towardsai.net
5 stars – based on 497 reviews

Frequently Used, Contextual References

TODO: Remember to copy unique IDs whenever it needs used. i.e., URL: 304b2e42315e

Resources

Unlock the full potential of AI with Building LLMs for Productionβ€”our 470+ page guide to mastering LLMs with practical projects and expert insights!

Publication

A Taxonomy of Retrieval Augmented Generation
Artificial Intelligence   Latest   Machine Learning

A Taxonomy of Retrieval Augmented Generation

Last Updated on November 3, 2024 by Editorial Team

Author(s): Abhinav Kimothi

Originally published on Towards AI.

Powering the rise of Contextual AI β€”Over 200 terms including Components, Pipelines, Ops Stack, Technologies & more

This member-only story is on us. Upgrade to access all of Medium.

Eight Themes of RAG Taxonomy (Source: Image by Author)

Retrieval Augmented Generation, or RAG, stands as a pivotal technique shaping the landscape of the applied generative AI. A novel concept introduced by Lewis et al in their seminal paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, RAG has swiftly emerged as a cornerstone, enhancing reliability and trustworthiness in the outputs from Large Language Models (LLMs).

In 2024, RAG is one of the most widely used techniques in generative AI applications.

As per Databricks, at least 60% of LLM applications utilise some form of RAG.

RAG’s acceptance is also propelled by the simplicity of the concept. Simply put, a RAG system searches for information from a knowledge base and sends it along with the query to the LLM for the response.

Retrieval Augmented Generation enhances the reliability and the trustworthiness in LLM responses (Source: Image by Author)

RAG today encompasses a wide array of techniques, models, and approaches. It can get a little overwhelming for newcomers. As RAG continues to evolve it’s crucial to create a shared language framework for researchers, practitioners, developers and business leaders. This taxonomy is an attempt to clarify the components of RAG,… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming aΒ sponsor.

Published via Towards AI

Feedback ↓