This is the code repository for Building Business-Ready Generative AI Systems, First Edition, published by Packt.
Denis Rothman
In today's rapidly evolving AI landscape, standalone LLMs no longer deliver sufficient business value on their own. This guide moves beyond basic chatbots, showing you how to build advanced, agentic ChatGPT-grade systems capable of sophisticated semantic and sentiment analysis, powered by context-aware AI controllers. You'll design AI controller architectures with multi-user memory retention, enabling your system to dynamically adapt to diverse user and system inputs. You'll architect a Retrieval-Augmented Generation (RAG) system with Pinecone, designed to combine instruction-driven scenarios. Enhance your system’s intelligence with powerful multimodal capabilities—including image generation, voice interactions, and machine-driven reasoning—leveraging Chain-of-Thought orchestration to address complex, cross-domain automation challenges. Seamlessly integrate generative models like OpenAI’s suite and DeepSeek-R1 without disrupting your existing GenAISys ecosystem. Your GenAISys will apply neuroscience-inspired insights to marketing strategies, predict human mobility, integrate smoothly into human workflows, visualize complex scenarios, and connect to live external data all wrapped in a polished, investor-ready interface. By the end, you'll have built a GenAISys capable of deploying intelligent agents in your business environment.
- Implement an AI controller with a conversation AI agent and orchestrator at its core
- Build contextual awareness with short-term, long-term, and cross-session memory
- Cross-domain automation with multimodal reasoning, image generation, and voice features
- Expand a CoT agent by integrating consumer-memory understanding
- Integrate cutting-edge models of your choice without disrupting your existing GenAISys
- Connect to real-time external data while blocking security breach
This repo is continually updated and upgraded.
📝 For details on updates and improvements, see the Changelog.
🐬 New bonus notebooks to explore, see Changelog.
🚩 If you see anything that doesn't run as expected, raise an issue, and we'll work on it!
You can run the notebooks directly from the table below:
| Chapters | Colab | Kaggle | Gradient | Studio Lab |
|---|---|---|---|---|
| Chapter 1: What is a ChatGPT AI Controller? | ||||
| Chapter 2: Building the Generative AI Model Controller | ||||
| Chapter 3: Adding Emerging Superalignment AI to the Generative AI Controller | ||||
| Chapter 4: Adding Multimodal RAG to the System | ||||
| Chapter 5: Adding Non-AI and ML Functionality to the Ecosystem | ||||
| Chapter 6: The Emergence of E-Marketing with AI Agents | ||||
| Chapter 7: The Emergence of Superintelligent Production Optimizing AI Agents | ||||
| Chapter 8: Implementing Warehouse and Transportation AI Agents | ||||
| Chapter 9: Intelligent Support Features | ||||
| Chapter 10: Integrating Advanced AI Agents into an Event- Driven Corporate System |
Denis Rothman Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide. LinkedIn