Published on: March 13th, 2024
Author: Deepali Rajale
3 min read
When creating a RAG (Retrieval Augmented Generation) system, you infuse a Large Language Model (LLM) with fresh, current knowledge. The goal is to make the LLM's responses to queries more factual and reduce instances that might produce incorrect or "hallucinated '' information.
A RAG system is a sophisticated blend of generative AI's creativity and a search engine's precision. It operates through several critical components working harmoniously to deliver accurate and relevant responses.
We can further break down this process into the following stages:
To enhance the effectiveness and precision of your RAG system, we recommend the following best practices:
By implementing these strategies, businesses can significantly improve the functionality and accuracy of their RAG systems, leading to more effective and efficient outcomes.
Using Karini AI’s purpose-built platform for GenAIOps, you can build production-grade, efficient RAG systems within minutes. Reach out to us to discuss your use case.
About the Author
Deepali Rajale is a founder of Karini AI with a mission to democratize generative AI across enterprises. She enjoys blogging about Generative AI, coaching customers to optimize Generative AI practice. She loves to spend time outdoors camping with her family and also a poet and has published a book.
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