New Summarization via In Context Learning with a New Class of Models

In this video I discuss some of the recent changes in building LLM apps and choosing which LLMs to use. I also show how I used some of these changes to build a Note taking app that creates summaries and long form notes For more tutorials on using LLMs and building Agents, check out my Patreon: Patreon: https://www.patreon.com/SamWitteveen Twitter: https://twitter.com/Sam_Witteveen 🕵️ Interested in building LLM Agents? Fill out the form below Building LLM Agents Form: https://drp.li/dIMes 👨‍💻Github: https://github.com/samwit/langchain-tutorials (updated) https://github.com/samwit/llm-tutorials ⏱️Time Stamps: 00:00 Intro 01:01 Personalization and Curation 01:18 Personalization 02:06 Curation 05:20 The State of LLMs 09:29 Long Output Use Cases 11:20 Claude 3: Haiku 12:10 Why Haiku 13:31 Haiku Challenges 14:23 Metaprompt 14:57 Haiku Exemplars 17:29 Summarizations 17:32 Types of Summarization 18:59 Simple Stuffing 19:21 Map Reduce 20:06 Refining our Calls 20:21 Map ReRank 20:31 New Summarization System 23:28 Sectioning 23:49 Advantages 24:17 Disadvantages 25:01 Conclusion