Secondbrain Meetup Hamburg

Retrieval Augmented Generation (RAG) is an innovative technique that enhances large language models by integrating external data retrieval, leading to more knowledgeable and contextual responses, and helps to mitigate the problem of "hallucination". Such a system can be very useful in Personal Knowledge Management (PKM).

Some use cases for RAG in PKM:

• Personal AI assistant that efficiently navigates through an individual's own data (documents, emails, notes, etc.) to answer queries or help craft customized content like emails or summaries.
• Question answering system that can fetch only the necessary details from relevant personal sources and craft informative responses that cite where the information was found.
• Document summarization to identify the main points in lengthy personal documents and distill them into short, digestible summaries.
• Content generation helpful in creating personal articles, reports or emails by incorporating pertinent information from various personal sources into a cohesive piece.
• Personalized recommendations (e.g. for products, services, content) by retrieving and analyzing an individual's preferences, past interactions, and other personal data.

Key Topics

• Leveraging LLMs for such a system development
• Enabling personalization and customization through LLM-driven user understanding
• Running RAG system locally or using cloud services
• Helpful guide to deploy such systems on your own computers or machines

Showcase of LLM-powered Solutions by WISERLI - Here the speaker takes the opportunity to present case studies where such systems are utilized:

• WiserStep: A personal finance management app that uses LLMs to provide personalized budgeting, investment, and savings recommendations.
• WiserBoard: A project management tool that leverages LLMs for task ideation for better team collaboration.

Keynote Speaker
Dr. Chandrakant Bothe [https://www.linkedin.com/in/chandrakantbothe], an expert in the field of AI and LLMs, takes a deep dive into real-world, and talks about cross-industry applications of such powerful AI models. The focus will be on how effective prompt engineering can help individuals to leverage existing LLM capabilities or create entirely new applications from scratch.

Because of request, Arthur repeats the talk he gave at February 2024.

You certainly don't want to give your most private data to a company, especially not your personal knowledge base. There are already some ways to run Chat bots similar to ChatGPT on your own hardware and this is exactly what we will explore in this event!
We will learn the different ways to setup your personal ChatGPT and how we might integrate it with our personal knowledge management app, e.g. Obsidian. We will also talk about and discuss these topics:
• What are the hardware requirements?
• How well do these models perform compared to ChatGPT?
• Can these local models do even more than ChatGPT?
• How does the future of local AI assistants look like?
There will be a live demonstration of the different ways to get it running on your own hardware and we will keep everything fun and interactive! We also hope to have an inspiring discussion with you.“