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In this article, I will introduce a development process can make an incredible difference in individual and team productivity. By focusing on this concept, my team and I have greatly increased our speed of delivery, and we are learning faster. It’s an important mindset and focus that, although quite simple and basic, is often overlooked or down prioritized. I hope you will find it as useful as I have. Below you can see the two development processes that will be introduces with more detail later in the article: ...

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. ...

One of the challenges we are faced with when building a chatbot is that questions the users ask the bot is highly unstructured. There are many ways different ways to ask for the same thing. Users can ask an extremely broad range of questions that require a diverse range of strategies to answer. The first step in building an helpful chatbot is to understand the user question well and putting it into a structured format that maps to entities that exist in the knowledge base. This can be done by doing intent classification and entity recognition. ...

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. ...