Advanced Retrieval Augmented Generation (RAG): Build a Powerful llm application using Re-ranker

Показать описание

"Mastering Retrieval Augmented Generation (RAG): Build a Powerful Questionnaire and Answer Application"

Description:
In this comprehensive tutorial, we'll dive deep into the world of Retrieval Augmented Generation (RAG), a cutting-edge technique that combines information retrieval and language modeling to generate detailed, human-like responses to user queries.

Throughout the video, you'll learn:

1. The fundamentals of Retrieval Augmented Generation (RAG) and its crucial role in the future of AI.
2. How to implement a RAG-powered questionnaire and answer application from scratch, using Python and the powerful Cohere API.
3. Techniques for data ingestion, text embedding, retrieval, and reranking to optimize the performance of your RAG system.
4. The importance of reranking in the RAG process and how it can significantly improve the accuracy and quality of your responses.
5. Real-world applications of Retrieval Augmented Generation (RAG) in various domains, including web search, question answering, and conversational AI.
6. Best practices and considerations for using Retrieval Augmented Generation (RAG) in your own projects.

The key focus of this tutorial will be on the importance of reranking in the RAG process. Reranking allows us to identify the most relevant information from the retrieved contexts, further boosting the accuracy and quality of the final response.

You'll learn:

How to ingest and process data, in this case, a Wikipedia article on Machine Learning.
Techniques for embedding text and computing similarity between the user's query and the text chunks.
The retrieval process, where we identify the most relevant chunks based on the query.
The reranking step, which intelligently filters and prioritizes the retrieved chunks.
The final response generation, where we leverage Cohere's language models to produce a coherent, informative answer..

So, if you're ready to unlock the full potential of Retrieval Augmented Generation (RAG), learn how to use Retrieval Augmented Generation (RAG) in Python, and explore the various applications of Retrieval Augmented Generation (RAG), including web search, question answering, and conversational AI, be sure to watch this comprehensive tutorial!