Conversational-Ai

This article is on using a knowledge graph for improving on a RAG system for complicated interconnected product information. The article will cover the problems with RAG, why knowledge graphs might help, and I will try to cover some design challenges of using knowledge graphs. Knowledge Graphs (KGs): A graph-based data structure that represents knowledge in a domain. It contains concepts (i.e. products) and the relationships between them (i.e. a products applications or spare parts). Using knowledge graphs, we can provide LLMs useful context needed for answering questions about pumps. ...

There are many choices to take when choosing the architecture for an LLM application, should you create a RAG flow, an agents or what about multi-agent architectures? This post will give an overview of the different types of LLM agents and how, why and if they should be used. Is the technology mature to be used in production systems? What are the challenges and can they be overcome? An agent can be used when a task is too complex to be solved by a single recipe. When there are multiple reasoning steps to solve the problem, and the steps are not always the same. Agents are relevant when they are put into a dynamic environment, where the agent needs to learn from its environment and adapt to it. ...

For consumers of advanced technical products, finding and understanding product documentation and learning about the systems can be a daunting task. With a wide range of products and complex engineering knowledge required, both our internal teams and customers often struggle to get the information they need. To address this challenge, we are developing an advanced AI chatbot designed to simplify access to documentation and provide quick answers to technical questions. This solution aims to enhance both internal efficiency and customer satisfaction. Over the past seven months, significant progress has been made, and as the AI architect, I am excited to share the results and insights gained from this project. ...

Introduction As more and more business problems can be solved using large language models (LLMs) in chat or QnA systems, the question of how to evaluate them has become increasingly important. Without proper evaluation, it is difficult to know if the system are providing real value to the business and users, or just misleading them and potentially inflicting harm. ...