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BUS-GENAI.AJ1
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.
- Practice in 9 Hands-On Labs — nothing to install
- 11 Interactive Lessons and 73 topics mapped to the official exam objectives
Expert Self-paced · 1 year access
9 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
11Interactive Lessons
73Topics
9LiveLab
50Flashcards
50Glossary of terms
01 / Lessons & labs
See exactly what you will learn and practice
Lessons
11 Interactive Lessons · 73 topics01 Introduction 3 topics +
- Who this course is for
- What this course covers
- To get the most out of this course
02 Defining a Business-Ready Generative AI System 6 topics · 2 LiveLab +
- Components of a business-ready GenAISys
- Business opportunities and scope
- Contextual awareness and memory retention
- Summary
- References
- Further reading
2 LiveLab in this lesson — see the labs panel →
03 Building the Generative AI Controller 6 topics · 2 LiveLab +
- Architecture of the AI controller
- Conversational AI agent
- AI controller orchestrator
- Summary
- References
- Further reading
2 LiveLab in this lesson — see the labs panel →
04 Integrating Dynamic RAG into the GenAISys 8 topics +
- 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
05 Building the AI Controller Orchestration Interface 7 topics · 1 LiveLab +
- 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
1 LiveLab in this lesson — see the labs panel →
06 Adding Multimodal, Multifunctional Reasoning with Chain of Thought 7 topics · 1 LiveLab +
- 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
1 LiveLab in this lesson — see the labs panel →
07 Reasoning E-Marketing AI Agents 6 topics · 1 LiveLab +
- Designing the consumer GenAISys memory agent
- Building the consumer memory agent
- GenAISys interface: From complexity to simplicity
- Summary
- References
- Further reading
1 LiveLab in this lesson — see the labs panel →
08 Enhancing the GenAISys with DeepSeek 11 topics +
- Balancing model evolution with project needs
- DeepSeek-V3, DeepSeek-R1, and R1-Distill-Llama: Overview
- Getting started with DeepSeek-R1-Distill-Llama-8B
- Implementing the handler selection mechanism as an orchestrator of the GenAISys
- IPython interface
- Handler selection mechanism
- Handler registry
- AI functions
- Summary
- References
- Further reading
09 GenAISys for Trajectory Simulation and Prediction 7 topics · 1 LiveLab +
- 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
1 LiveLab in this lesson — see the labs panel →
10 Upgrading the GenAISys with Data Security and Moderation for Customer Service 7 topics +
- 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 5 topics · 1 LiveLab +
- Designing the presentation of the GenAISys
- Building a flexible HTML interface
- Summary
- References
- Further reading
1 LiveLab in this lesson — see the labs panel →
Hands-On Labs Our edge
9 LiveLabs- Executing a Query in a Stateless Session
- Implementing Manual Session-Based Memory Handling in AI Query Execution
- Creating a Conversational AI Agent with Short-Term Memory Retention
- Creating a Conversational AI Agent with Long-Term Memory Retention
- Creating an Event-Driven GenAISys Framework
- Implementing Multimodal Reasoning Using CoT
- Implementing a Consumer Memory Agent
- Adding a Generic Synthetic Trajectory Simulation and Predictive System to the GenAISys
- Orchestrating a Swarm
Labs run in your browser — nothing to install.
02 / FAQs
Questions before you start
Is this course only for developers?+
While it’s technically focused, understanding the architectural decisions is key. Some sections require coding experience to implement fully, others are conceptual.
How much prior AI experience do I need? +
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.
Does this cover specific vendor tools extensively?+
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.
Will this course help me deploy a GenAI system immediately?+
It provides the architectural blueprint and practical steps. Actual deployment still depends heavily on your specific environment, data, and organizational hurdles.
Keep going
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Stop Prototyping. Start Architecting.
Master RAG, orchestration, and security to build resilient, business-ready GenAI. Get the technical blueprints to deploy with confidence.
- 1 year of full access
- 9 LiveLab included
- Certificate of completion
No credit card required