🤔 Struggling to get all the results you want from your RAG app? Here’s how to fix it! When you have a large collection of documents, getting all the results you need can be challenging. Here’s a common scenario: 1️⃣ You have an index with thousands of documents on similar topics. 2️⃣ You query a specific topic that is mentioned in several documents, but only a few focus extensively on it. 3️⃣ Chunks from the few focused documents dominate your results, even though you want to see relevant information from all the documents. 𝗪𝗲 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱 𝘀𝗲𝘁𝘁𝗶𝗻𝗴 𝗮 𝗵𝗶𝗴𝗵 𝗞 𝘃𝗮𝗹𝘂𝗲 𝘁𝗼 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲 𝗯𝗿𝗼𝗮𝗱𝗲𝗿 𝗿𝗲𝘀𝘂𝗹𝘁𝘀. However, this approach can introduce noise. To refine your results, you can use a reranker to cut off the low-ranking results and increase precision. This is how you would do it from scratch, which requires experimentation to find what works best. Keep in mind that this will increase latency. With trufflepig, you don't need to compromise on latency to get the results you need. 👋 Have you faced similar challenges? Share your thoughts in the comments and follow trufflepig for more AI content 🐷 Try trufflepig: (https://lnkd.in/gdW98A38)
Adam Tazi
6mo
all my homies hate latency
Try trufflepig! https://bit.ly/4cdhIbX