Defining a Business-Ready Generative AI System

Implementing a generative AI system (GenAISys) in an organization doesn’t stop at simply integrating a standalone model such as GPT, Grok, Llama, or Gemini via an API. While this is often a starting point, we often mistake it as the finish line. The rising demand for AI, as it expands across all domains, calls for the implementation of advanced AI systems that go beyond simply integrating a prebuilt model.

A business-ready GenAISys should provide ChatGPT-grade functionality in an organization, but also go well beyond it. Its capabilities and features must include natural language understanding (NLU), contextual awareness through memory retention across dialogues in a chat session, and agentic functions such as autonomous image, audio, and document analysis and generation. This requires thoughtful context engineering, where we strategically manage the information given to the model to guide its responses. Think of a generative AI model as an entity with a wide range of functions, including AI agents as agentic co-workers.

We will begin the chapter by defining what a business-ready GenAISys is. From there, we’ll focus on the central role of a generative AI model, such as GPT-4o, that can both orchestrate and execute tasks. Building on that, we will lay the groundwork for contextual awareness and memory retention, discussing four types of generative AI memory: memoryless, short-term, long-term, and multiple sessions. We will also define a new approach to retrieval-augmented generation (RAG) that introduces an additional dimension to data retrieval: instruction and agentic reasoning scenarios. Adding instructions stored in a vector store takes RAG to another level by retrieving instructions that we can add to a prompt. In parallel, we will examine a critical component of a GenAISys: human roles. We will see how, throughout its life cycle, an AI system requires human expertise. Additionally, we will define several levels of implementation to adapt the scope and scale of a GenAISys, not only to business requirements but also to available budgets and resources.

Finally, we’ll illustrate how contextual awareness and memory retention can be implemented using OpenAI’s LLM and multimodal API. A GenAISys cannot work without solid memory retention functionality—without memory, there’s no context, and without context, there’s no sustainable generation. Throughout this book, we will create modules for memoryless, short-term, long-term, and multisession types depending on the task at hand. By the end of this chapter, you will have acquired a clear conceptual framework for what makes an AI system business-ready and practical experience in building the first bricks of an AI controller.

In a nutshell, this chapter covers the following topics:

• Components of a business-ready GenAISys
• AI controllers and agentic functionality (model-agnostic)
• Hybrid human roles and collaboration with AI
• Business opportunities and scope
• Contextual awareness through memory retention

Let’s begin by defining what a business-ready GenAISys is.