http://localhost:1313/chapter1/index.html Work in progress This course is under construction. This information hasn’t been reviewed or edited yet! Is this Chapter for You? One of the core needs for technical professionals is to keep up with emerging technologies that are transforming how we build and deploy software. AI and LLMs represent a fundamental shift in what’s possible with code, but coming with their own concepts, terminology, and best practices - it can be overwhelming to know where to start! Hugo en http://localhost:1313/chapter1/s1/index.html Mon, 01 Jan 0001 00:00:00 +0000 http://localhost:1313/chapter1/s1/index.html Work in progress This section is under construction. This information hasn’t been reviewed or edited yet! TL;DR Too long to read? Prefer to listen to this section? We got you covered! This is a version of this section as an audio postcast produced using Google’s NotebookLM. Using AI to teach you AI, how meta! Your browser does not support the audio element. Alternatively, if you feel like you know this already, try your hand at the optional quiz below and see how you do. Or you can just skip to the next section. We won’t judge you! http://localhost:1313/chapter1/s2/index.html Mon, 01 Jan 0001 00:00:00 +0000 http://localhost:1313/chapter1/s2/index.html Work in progress This section is under construction. This information hasn’t been reviewed or edited yet! Introduction Now that we’ve explored the foundational architecture of large language models (LLMs), let’s take a step back and look at the landscape of key players and models shaping this transformative technology. Whether you’re considering commercial solutions, open-source options, or local deployments, understanding the ecosystem is essential for selecting the right tool for your needs. http://localhost:1313/chapter1/s3/index.html Mon, 01 Jan 0001 00:00:00 +0000 http://localhost:1313/chapter1/s3/index.html Work in progress This section is under construction. This information hasn’t been reviewed or edited yet! Introduction The AI landscape can be confusing when it comes to deployment choices, particularly because similar names often mask very different security and operational implications. For instance, when someone mentions “using GPT,” they might be referring to ChatGPT’s web interface, OpenAI’s API service, or Azure’s enterprise deployment—each with vastly different security profiles and use cases. In some cases, people even confuse the term “ChatGPT” with the underlying GPT model itself, further complicating conversations. http://localhost:1313/chapter1/s4/index.html Mon, 01 Jan 0001 00:00:00 +0000 http://localhost:1313/chapter1/s4/index.html Work in progress This section is under construction. This information hasn’t been reviewed or edited yet! Introduction Now that we’ve explored how AI evolved into its current form, let’s lift the hood and examine the engine that powers large language models (LLMs). These systems are marvels of engineering, built on a foundation of interconnected components that work together to process and generate human-like text. http://localhost:1313/chapter1/s5/index.html Mon, 01 Jan 0001 00:00:00 +0000 http://localhost:1313/chapter1/s5/index.html Work in progress This section is under construction. This information hasn’t been reviewed or edited yet! Introduction At their core, LLMs work by responding to “prompts” - text inputs that tell the model what we want it to do. Think of a prompt as a conversation starter or instruction that guides the AI’s response. However, there’s more complexity to prompts than meets the eye, especially when working with different API types and managing conversations. http://localhost:1313/chapter1/s6/index.html Mon, 01 Jan 0001 00:00:00 +0000 http://localhost:1313/chapter1/s6/index.html Work in progress This section is under construction. This information hasn’t been reviewed or edited yet! Introduction Now that we’ve explored the fundamentals of LLMs, key players in the market, deployment considerations, technical foundations, and the art of prompt engineering, it’s time to dive into how these models actually operate in real-world applications. This section will examine the technical aspects of inference—the process where LLMs generate responses to our inputs—focusing on API integration patterns, response handling strategies, knowledge integration techniques, and optimization methods. Understanding inference techniques is crucial for implementing LLMs effectively, whether you’re building a simple chatbot or a sophisticated enterprise application with access to proprietary knowledge. http://localhost:1313/chapter1/s7/index.html Mon, 01 Jan 0001 00:00:00 +0000 http://localhost:1313/chapter1/s7/index.html Work in progress This section is under construction. This information hasn’t been reviewed or edited yet! Introduction Throughout this chapter, we’ve explored the foundations of AI systems, from understanding their core architectures to examining deployment strategies, technical underpinnings, crafting effective prompts, and implementing inference techniques. Now, we turn our attention to what many consider the next frontier in AI evolution: agentic systems. While current LLMs excel at generating content and retrieving knowledge, agentic AI goes further by autonomously pursuing goals, making decisions, and taking actions without constant human guidance. This shift from passive tools to proactive agents represents a fundamental transformation that will redefine how organizations leverage AI and has profound implications for security, governance, and the future of work.