Generative AI Application Integration Patterns: Integrate large language models into your applications
by Juan Pablo Bustos and Luis Lopez Soria
Integrate large language models into your applications
This is the code repository for Generative AI Application Integration Patterns, published by Packt.
Explore the transformative potential of Generative AI (GenAI) in the application development lifecycle. Through concrete examples, you will go through the process of ideation and integration, understanding the tradeoffs and the decision points when integrating GenAI.
With recent advances in models like Google Gemini, Anthropic Claude, DALL-E and GPT-4o, this timely resource will help you harness these technologies through proven design patterns.
We then delve into the practical applications of GenAI, identifying common use cases and applying design patterns to address real-world challenges. From summarization and metadata extraction to intent classification and question answering, each chapter offers practical examples and blueprints for leveraging GenAI across diverse domains and tasks. You will learn how to fine-tune models for specific applications, progressing from basic prompting to sophisticated strategies such as retrieval augmented generation (RAG) and chain of thought.
Additionally, we provide end-to-end guidance on operationalizing models, including data prep, training, deployment, and monitoring. We also focus on responsible and ethical development techniques for transparency, auditing, and governance as crucial design patterns.
- Concepts of GenAI: pre-training, fine-tuning, prompt engineering, and RAG
- Framework for integrating AI: entry points, prompt pre-processing, inference, post-processing, and presentation
- Patterns for batch and real-time integration Code samples for metadata extraction, summarization, intent classification, question-answering with RAG, and more
- Ethical use: bias mitigation, data privacy, and monitoring
- Deployment and hosting options for GenAI models
- Introduction to Generative AI Patterns
- Identifying Generative AI Use Cases
- Designing Patterns for Interacting with Generative AI
- Generative AI Batch and Real-time Integration Patterns
- Integration Pattern: Batch Metadata Extraction
- Integration Pattern: Batch Summarization
- Integration Pattern: Real-Time Intent Classification
- Integration Pattern: Real-Time Retrieval Augmented Generation
- Operationalizing Generative AI Integration Patterns
- Embedding Responsible AI into your GenAI Applications
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Clone the repository:
git clone https://github.com/PacktPublishing/Generative-AI-Integration-Patterns-1E
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Navigate to the directory:
cd Generative-AI-Integration-Patterns-1E -
Set up a virtual environment:
python3 -m venv .venv source .venv/bin/activate -
Install dependencies:
pip install -r requirements.txt
You can also run the notebooks directly from the table below:
| Chapter | Kaggle | Colab |
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| Chapter 5: Integration Pattern: Batch Metadata Extraction | ||
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| Chapter 6: Integration Pattern: Batch Summarization | ||
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| Chapter 7: Integration Pattern: Real-Time Intent Classification | ||
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| Chapter 8: Integration Pattern: Real-Time Retrieval Augmented Generation | ||
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If you see anything that doesn't run as expected, raise an issue, and we'll work on it!
You can create an issue
, if you encounter any in the notebooks, we will be glad to provide you support.
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Juan Pablo Bustos is a seasoned technology professional specializing in artificial intelligence and machine learning. With a background in computer science, Juan has held leadership positions at major tech companies including Google, Stripe, and Amazon Web Services. His expertise spans AI services, solution architecture, and cloud computing. Juan is passionate about helping organizations leverage cutting-edge technologies to drive innovation and deliver value. LinkedIn
Luis Lopez Soria is an experienced software architect specializing in AI/ML. He has gained practical experience from top firms across heavily regulated industries like healthcare and finance, as well as big tech firms like AWS and Google. He brings a blended approach from his experience managing global partnerships, AI product development, and customer-facing roles. Luis is passionate about learning new technologies and using these to create a business value. LinkedIn