This course equips learners with the knowledge and skills to build intelligent applications using generative AI. It dives deep into the AI stack, covering large language models (LLMs), vector databases, and Python frameworks. Learners will also explore strategies to enhance AI performance and reliability, critical in today’s rapidly evolving AI landscape.
Building AI Intensive Python Applications
Building AI Intensive Python Applications
Instructor: Packt - Course Instructors
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Intermediate level
Recommended experience
3 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Understand the architecture of the generative AI stack
Explore the role of vector databases in AI applications
Implement Python frameworks for AI development
Skills you'll gain
- Prompt Engineering
- Artificial Intelligence
- Vector Databases
- Large Language Modeling
- Python Programming
- Data Modeling
- LLM Application
- Model Evaluation
- Application Design
- AI Security
- Embeddings
- Natural Language Processing
- Generative AI
- Retrieval-Augmented Generation
- Metadata Management
- MongoDB
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Assessments
12 assignments
Taught in English
Recently updated!
February 2026
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There are 12 modules in this course
In this section, we explore generative AI fundamentals, including its stack components, Python integration, and ethical considerations, to guide practical web development applications.
What's included
2 videos2 readings1 assignment
In this section, we explore the building blocks of intelligent applications, including LLMs, vector embeddings, and model hosting, to enable context-aware, adaptive software solutions.
What's included
1 video3 readings1 assignment
In this section, we explore n-gram models, artificial neural networks, and Transformer architecture to understand large language models' implementation and applications in natural language processing.
What's included
1 video7 readings1 assignment
In this section, we explore embedding models, their applications in NLP and data processing, and how to implement them using Python for semantic search and vector analysis.
What's included
1 video4 readings1 assignment
In this section, we cover vector databases, embeddings, and their role in AI search and retrieval systems.
What's included
1 video9 readings1 assignment
In this section, we explore data modeling, storage, and secure data flow for AI/ML applications, emphasizing practical implementation and RBAC principles for efficient and secure system design.
What's included
1 video4 readings1 assignment
In this section, we explore Python-based AI/ML frameworks, libraries, and APIs for building generative AI applications, focusing on real-world data integration and retrieval-augmented generation solutions.
What's included
1 video5 readings1 assignment
In this section, we explore integrating vector search with RAG systems, focusing on efficient data retrieval and enhancing AI application intelligence through practical techniques.
What's included
1 video5 readings1 assignment
In this section, we explore LLM evaluation strategies, focusing on metrics, guardrails, and reliability in intelligent applications to ensure effective and safe AI deployment.
What's included
1 video9 readings1 assignment
In this section, we explore techniques to refine semantic data models for improved accuracy in retrieval-augmented generation (RAG) applications. Key concepts include embedding model experimentation, metadata optimization, and advanced retrieval systems.
What's included
1 video5 readings1 assignment
In this section, we examine common GenAI failure modes, including hallucinations, sycophancy, data leakage, and performance issues, to improve accuracy and reliability in practical applications.
What's included
1 video6 readings1 assignment
In this section, we explore techniques to improve GenAI application reliability, including baselining, dataset design, and feedback loops for optimized performance and stable outputs.
What's included
1 video5 readings1 assignment
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