Kernel Memory - Nlp, Precision, Recall, ReRanking 

In a RAG system the R part (Retrieve) is the most important part, because is the one where you extract from all your knowledge, the pieces of text that you are going to pass to a LLM to answer the user question.
As you can understand, it is important that this search is precise. But what is precision? What is Recall? Can you have both of them given latest advancement in the AI from latest years? In this video I'm trying to give you an introduction on these arguments, in the next one I'll put everything into practice with Kernel Memory.
▬ Contents of this video ▬▬▬▬▬▬▬▬▬▬
00:00 - Introduction to Retrieval Augmented Generation
00:43 - Importance of Precision and Recall in Natural Language Processing
04:00 - Explanation of Precision and Recall Metrics
05:45 - Introduction of Large-Language Model and Encoder-Decoder Structures
06:23 - Challenges with Traditional Search Methods
07:57 - Importance of Precision in Search Results
10:29 - Introduction of Cross Encoding
13:46 - Introduction of Ranking in Cross Encoding
18:49 - Advanced Approach to Ranking in Retrieval Augmented Generation
20:58 - Importance of Introducing ReRanking in Retrieval Augmented Generation Pipeline

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