In this section, we introduce Large Language Models (LLMs), discuss their role in generative AI, compare LLM architectures with classical machine learning, and explain the distinction between base and fine-tuned LLMs for real-world applications.

In this section, we examine how large language models (LLMs) are transforming software development, explore the architecture of copilot systems, and evaluate AI orchestrator frameworks for embedding LLMs in real-world applications.

In this section, we examine the criteria for selecting large language models (LLMs), comparing architectures, performance, costs, and real-world trade-offs to optimize application integration and responsible use.

In this section, we introduce prompt engineering techniques to create effective prompts that guide large language model behavior and help reduce bias and hallucinations.

In this section, we demonstrate how to embed large language models (LLMs) in applications using LangChain, integrate Hugging Face models, and leverage frameworks for enhanced conversational user experiences.

In this section, we build LLM-based conversational applications using LangChain, adding memory, non-parametric knowledge, and tools, while developing a Streamlit front-end for rapid prototyping and practical deployment.

In this section, we examine how large language models (LLMs) modernize recommendation systems, discuss traditional and LLM-powered techniques, and implement practical applications using LangChain and Streamlit for interactive user experiences.

In this section, we demonstrate how to integrate large language models (LLMs) with relational databases, enabling natural language interfaces to tabular data and combining structured with unstructured sources for practical applications.

In this section, we explore how Large Language Models (LLMs) support code generation, understanding, and algorithm emulation, enabling the development of natural language-driven programming tools and code-based applications.

In this section, we learn to build adaptive multimodal agents by integrating language, image, and audio models using LangChain and Azure AI, enabling robust, practical AI workflows and applications.

In this section, we examine the theory and practical steps for fine-tuning large language models (LLMs), covering data preparation, domain-specific taxonomy, and implementation using Python and Hugging Face for specialized NLP applications.

In this section, we examine Responsible AI practices for mitigating risks and biases in large language model (LLM) applications, exploring architectural strategies and key regulatory requirements to ensure safer AI deployment.

In this section, we examine recent innovations in large language models (LLMs) and generative AI, explore enterprise adoption, and discuss applications such as GPT-4V(ision), AutoGen, and small language models for future-ready development.