Retrieval augmented generation has proven to be quite an effective technique to achieve good results with LLMs, so that they may provide answers based on your own data.
While retrieval is a key step in such applications, other step have also started to show promise for various use cases: Ranking. In this session we will discuss why retrieval and ranking play important roles to build effective applications with LLMs. In particular, we will see how we can use Lost in the Middle and Diversity Rankers with Haystack, an open source LLM framework, to improve the quality of our RAG pipeline results. We will also briefly discuss the role of hybrid retrieval