Overview

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. You will gain hands-on experience by building AI-powered applications, learning how to implement vector databases for data retrieval and enhance models with Python. With a practical, step-by-step approach, you will develop the expertise to create intelligent apps that can adapt to real-world challenges. What sets this course apart is its focus on both theoretical concepts and real-world application. You’ll work on practical use cases, from AI architecture to the integration of LLMs, vector databases, and Python frameworks, giving you the confidence to implement AI solutions in various industries. This course is perfect for software engineers and developers with a basic understanding of Python who want to build intelligent applications using generative AI. It’s designed to provide both foundational knowledge and practical skills to boost AI performance and reliability.

Syllabus

Getting Started with Generative AI

In this section, we explore generative AI fundamentals, including its stack components, Python integration, and ethical considerations, to guide practical web development applications.

Building Blocks of Intelligent Applications

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.

Large Language Models

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.

Embedding Models

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.

Vector Databases

In this section, we cover vector databases, embeddings, and their role in AI search and retrieval systems.

AI/ML Application Design

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.

Useful Frameworks, Libraries, and APIs

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.

Implementing Vector Search in AI Applications

In this section, we explore integrating vector search with RAG systems, focusing on efficient data retrieval and enhancing AI application intelligence through practical techniques.

LLM Output Evaluation

In this section, we explore LLM evaluation strategies, focusing on metrics, guardrails, and reliability in intelligent applications to ensure effective and safe AI deployment.

Refining the Semantic Data Model to Improve Accuracy

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.

Common Failures of Generative AI

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.

Correcting and Optimizing Your Generative AI Application

In this section, we explore techniques to improve GenAI application reliability, including baselining, dataset design, and feedback loops for optimized performance and stable outputs.