This course explores how to effectively integrate Generative AI into application development, covering key concepts, practical strategies, and real-world applications. You'll gain insights into designing, deploying, and managing AI-driven solutions with a focus on ethical and responsible practices.
Generative AI Application Integration Patterns
Generative AI Application Integration Patterns
Instructor: Packt - Course Instructors
Access provided by ExxonMobil
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand core GenAI concepts such as pre-training, fine-tuning, and RAG.
Apply frameworks for integrating AI into applications through real-world examples.
Implement ethical practices for responsible AI development and deployment.
Skills you'll gain
- Deep Learning
- MLOps (Machine Learning Operations)
- Real Time Data
- Cloud Development
- AI Enablement
- Model Deployment
- Scalability
- Artificial Intelligence
- Prompt Engineering
- LLM Application
- Cloud Computing Architecture
- Generative Model Architectures
- ChatGPT
- Data Ethics
- Responsible AI
- AI Security
- Generative AI
- Skills section collapsed. Showing 10 of 17 skills.
Details to know
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Assessments
10 assignments
Taught in English
Recently updated!
December 2025
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There are 10 modules in this course
In this section, we explore generative AI integration patterns, transformer and diffusion model architectures, and responsible experimentation strategies to enable practical AI applications.
What's included
2 videos4 readings1 assignment
In this section, we explore methods to identify generative AI (GenAI) use cases using interaction frameworks, analyze business value, and differentiate between comprehensive and generative applications.
What's included
1 video3 readings1 assignment
In this section, we cover integrating GenAI into workflows, focusing on real-time interactions, prompt processing, and model monitoring for continuous improvement.
What's included
1 video3 readings1 assignment
In this section, we explore batch and real-time integration patterns for LLMs, focusing on throughput, latency, and pipeline design for practical system implementation.
What's included
1 video3 readings1 assignment
In this section, we explain how to extract metadata from 10-K reports using GenAI and cloud tools.
What's included
1 video4 readings1 assignment
In this section, we explore Generative AI for financial document summarization and cloud integration.
What's included
1 video2 readings1 assignment
In this section, we cover real-time intent classification and AI integration for fast user interactions.
What's included
1 video3 readings1 assignment
In this section, we cover RAG systems for financial services, including use cases, architecture, and implementation.
What's included
1 video1 reading1 assignment
In this section, we explore operationalizing GenAI integration patterns with a focus on scalability and compliance.
What's included
1 video8 readings1 assignment
In this section, we examine responsible AI practices for GenAI applications, focusing on fairness, interpretability, privacy, and security to ensure ethical and compliant AI systems.
What's included
1 video8 readings1 assignment
Instructor
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