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Building Business-Ready Generative AI Systems

  Build reliable Generative AI systems with confidence. Master Retrieval-Augmented Generation (RAG), orchestration workflows, and AI security through structured learning—moving from basic AI chains to scalable, business-ready solutions.

(BUS-GENAI.AJ1) / ISBN : 979-8-90059-104-9
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About This Course

Building Business-Ready Generative AI Systems means grappling with real-world mess. You're not just chaining models; you’re making them reliable for actual users, which is harder than it sounds. Things break when context drifts or data isn't handled right.

This course dives into how to architect these systems, focusing on the points where they usually fall apart. We look at RAG, orchestration, and security. There are 95 Practice Quizzes and 11 Comprehensive Chapters to help.It won't solve every organizational politics issue, or guarantee perfect adoption, but it gives you tools to manage the technical complexity. You'll see why the 'easy' parts often cause the biggest headaches.

Skills You’ll Get

  • GenAI Controller Orchestration: Systems become brittle, unable to manage complex user flows or integrate multiple AI functions reliably.
  • Dynamic RAG Implementation: Information retrieval fails under novel queries, leading to hallucinations or irrelevant responses when data changes.
  • Multimodal CoT Reasoning: The system can't handle diverse input types or complex, multi-step problems, stuck on simple text prompts.
  • Security and Moderation Integration: Data leaks, compliance issues, or inappropriate content generation risks undermine trust and deployment viability.
Download Course Outline

1

Introduction

  • Who this course is for
  • What this course covers
  • To get the most out of this course
2

Defining a Business-Ready Generative AI System

  • Components of a business-ready GenAISys
  • Business opportunities and scope
  • Contextual awareness and memory retention
  • Summary
  • References
  • Further reading
3

Building the Generative AI Controller

  • Architecture of the AI controller
  • Conversational AI agent
  • AI controller orchestrator
  • Summary
  • References
  • Further reading
4

Integrating Dynamic RAG into the GenAISys

  • Architecting RAG for dynamic retrieval
  • Building a dynamic Pinecone index
  • Upserting instruction scenarios into the index
  • Upserting classical data into the index
  • Querying the Pinecone index
  • Summary
  • References
  • Further reading
5

Building the AI Controller Orchestration Interface

  • Architecture of an event-driven GenAISys interface
  • Building the processes of an event-driven GenAISys interface
  • Conversational agent
  • Multi-user, multi-turn GenAISys session
  • Summary
  • References
  • Further reading
6

Adding Multimodal, Multifunctional Reasoning with Chain of Thought

  • Enhancing the event-driven GenAISys interface
  • Setting up the environment
  • Image generation and analysis
  • Reasoning with CoT
  • Running CoT reasoning from a user perspective
  • Summary
  • References
7

Reasoning E-Marketing AI Agents

  • Designing the consumer GenAISys memory agent
  • Building the consumer memory agent
  • GenAISys interface: From complexity to simplicity
  • Summary
  • References
  • Further reading
8

Enhancing the GenAISys with DeepSeek

  • Balancing model evolution with project needs
  • DeepSeek-V3, DeepSeek-V1, and R1-Distill-Llama: Overview
  • Getting started with DeepSeek-R1-Distill-Llama-8B
  • Implementing the handler selection mechanism as an orchestrator of the GenAISys
  • 1. IPython interface
  • 2. Handler selection mechanism
  • 3. Handler registry
  • 4. AI functions
  • Summary
  • References
  • Further reading
9

GenAISys for Trajectory Simulation and Prediction

  • Trajectory simulations and predictions
  • Building the trajectory simulation and prediction function
  • Adding mobility intelligence to the GenAISys
  • Running the mobility-enhanced GenAISys
  • Summary
  • References
  • Further reading
10

Upgrading the GenAISys with Data Security and Moderation for Customer Service

  • Enhancing the GenAISys
  • Adding a security function to the handler selection mechanism
  • Building a weather forecast component
  • Running the GenAISys
  • Summary
  • References
  • Further reading
11

Presenting Your Business-Ready Generative AI System

  • Designing the presentation of the GenAISys
  • Building a flexible HTML interface
  • Summary
  • References
  • Further reading

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While it’s technically focused, understanding the architectural decisions is key. Some sections require coding experience to implement fully, others are conceptual.

A basic grasp of machine learning concepts helps, especially around large language models. We dive into specifics, but foundational knowledge makes the ramp-up smoother.

We use tools like Pinecone for RAG examples, and DeepSeek for model comparison. The principles are transferable, but specific implementations are shown with chosen platforms.

It provides the architectural blueprint and practical steps. Actual deployment still depends heavily on your specific environment, data, and organizational hurdles.

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