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Have you ever jumped straight into coding, only to realize later that you built the wrong thing? - I have. Many times. In fact, I tend to do it so often that I’ve started thinking about why it happens—and how I can avoid it. One reason, I’ve found, is that I often get stuck drinking too much from the wrong mug. Wait, mugs? Yes, mugs. Let me explain. 🎩 First, a sip from Edward de Bono Let’s take a moment to appreciate the wisdom of Edward de Bono, a renowned psychologist and author who introduced the concept of “Six Thinking Hats.” His idea is that we can improve our thinking and decision-making by consciously adopting different perspectives, each represented by a colored hat. Each hat represents a different way of thinking: ...

In this article, I will introduce a development process can make an incredible difference in individual and team productivity. By focusing on getting feedback faster with this concept, my team and I have greatly increased our speed of delivery, and we are learning faster. The focus upon fast feedback is an important mindset that, although quite simple and basic, is often overlooked or down prioritized. By introducing the differentiation between inner and outer development loops, it becomes much easier to discover when faster feedback is needed and how to achieve it. I will introduce a development process tweaked for AI project. The framework will help you to identify what habits and practices to focus on to improve your development process both for traditional software development and for developing AI projects. ...

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. ...