What is Retrieval Augmented Generation?

Google Cloud Tech · Beginner ·🔍 RAG & Vector Search ·2y ago
Skills: RAG Basics90%

Key Takeaways

Retrieval-augmented generation (RAG) combines large language models with external knowledge bases to improve accuracy, using techniques like tax bedding and combining prompts with retrieved context to generate final outputs.

Full Transcript

what is retrieval augmented generation also known as RG or grounding R is a new AI framework that combines large language models with external knowledge bases to improve the accuracy of model response imagine you have a Wikipedia page on T Swift that you like all your answers to gr it on it's your source of Truth RG retrieves information through the Wikipedia API using tax beding combine Dural prompt with retrieve contacts send it to the LM to generate the final output for example1 as LM how many record had Terra Swift sold globally if the isn't tra on the most recent data the model give you a wrong response by using Wikipedia API as my knowledge base I can secure the correct response because it's grounded on the given knowledge base is like a cheat sheet for the ads check out the RG blog post yourself and share the comments down below

Original Description

Retrieval-augmented generation (RAG) is a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. Check out the blog for more details!
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Retrieval-augmented generation (RAG) is a technique that enhances the accuracy of generative AI models by combining large language models with external knowledge bases. This technique uses tax bedding and combining prompts with retrieved context to generate final outputs, providing a more reliable and accurate response. By using RAG, developers can improve the performance of their AI models and provide more accurate information to users.

Key Takeaways
  1. Define the knowledge base to use for grounding
  2. Use tax bedding to combine prompts with retrieved context
  3. Send the combined prompt to the large language model
  4. Generate the final output based on the model's response
  5. Test and evaluate the accuracy of the RAG model
💡 RAG provides a way to ground generative AI models in external knowledge bases, improving their accuracy and reliability by providing a 'cheat sheet' for the models to draw upon.

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