# KodeKloud: Beta Courses Generative AI in Practice: Advanced Insights and Operations

## Generative AI in Practice: Advanced Insights and Operations

### Evolution of AI Models

- [Story of Artificial Intelligence](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Evolution-of-AI-Models/Story-of-Artificial-Intelligence/page.md): This lesson explores artificial intelligence, focusing on large language models, generative AI, and the evolution of computing paradigms leading to self-supervised learning.
- [Neural Networks](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Evolution-of-AI-Models/Neural-Networks/page.md): Neural networks are a key component of AI, learning complex functions through interconnected nodes inspired by the human brain.
- [Transformers](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Evolution-of-AI-Models/Transformers/page.md): This article discusses transformers, their mechanisms, applications, challenges, and the hardware enabling their capabilities in modern AI.
- [Training Model Fine Tuning](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Evolution-of-AI-Models/Training-Model-Fine-Tuning/page.md): This article explores the steps involved in training and fine-tuning models like GPT-4 using neural networks and the Transformer architecture.
- [Multimodality Responsible AI](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Evolution-of-AI-Models/Multimodality-Responsible-AI/page.md): This article explores multimodality in AI, its relationship with responsible practices, and the implications for processing diverse data types.

### Retrieval-Augmented Generation (RAG)

- [Introduction to RAG](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Retrieval-Augmented-Generation-RAG/Introduction-to-RAG/page.md): This lesson explores Retrieval Augmented Generation as a solution to the limitations of foundation models by integrating real-time data retrieval with generative capabilities.
- [Vector Search](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Retrieval-Augmented-Generation-RAG/Vector-Search/page.md): This article explores vector search, its techniques, and applications in AI, emphasizing the importance of embeddings for retrieving semantically relevant information.
- [Naive RAG](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Retrieval-Augmented-Generation-RAG/Naive-RAG/page.md): This article introduces Naive RAG, a simple method for Retrieval-Augmented Generation, detailing its workflow, advantages, limitations, and techniques for effective document chunking.
- [Advanced Agentic RAG](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Retrieval-Augmented-Generation-RAG/Advanced-Agentic-RAG/page.md): This article explores advanced agentic Retrieval-Augmented Generation systems that dynamically retrieve information to improve query outcomes and overall performance.
- [Graph RAG](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Retrieval-Augmented-Generation-RAG/Graph-RAG/page.md): This article explores the integration of graph databases and large language models to enhance knowledge retrieval systems.
- [Demo RAG Application](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Retrieval-Augmented-Generation-RAG/Demo-RAG-Application/page.md): This article provides a guide for deploying a production-grade retrieval-augmented generation application using Azure, focusing on a chatbot for e-commerce customer support.

### AI Application(LLM) on Azure

- [AI ApplicationLLM on Azure Part 1](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/AI-ApplicationLLM-on-Azure/AI-ApplicationLLM-on-Azure-Part-1/page.md): This article explores building a test application using Retrieval-Augmented Generation on Azure, detailing infrastructure, code integration, and API setup.
- [AI ApplicationLLM on Azure Part 2](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/AI-ApplicationLLM-on-Azure/AI-ApplicationLLM-on-Azure-Part-2/page.md): This article explores building a production-ready AI application on Azure using containerized services, API endpoints, dynamic agents, and evaluation metrics.

### Large Language Models(LLM)

- [Introduction to LLMs and its Types](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Large-Language-ModelsLLM/Introduction-to-LLMs-and-its-Types/page.md): This lesson introduces large language models, their architecture, types, and ethical considerations in Generative AI, providing insights and real-world use cases.
- [Introduction to Transformer Architecture](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Large-Language-ModelsLLM/Introduction-to-Transformer-Architecture/page.md): This article explores the inner workings of Transformer models, their performance on GPUs, and the components that contribute to their language understanding capabilities.
- [Tokens and Tokenization](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Large-Language-ModelsLLM/Tokens-and-Tokenization/page.md): This article explores tokens and tokenization, essential concepts for understanding how large language models process language in natural language processing.

### Prompting Techniques in LLM

- [Zero Shot Prompting](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Prompting-Techniques-in-LLM/Zero-Shot-Prompting/page.md): This article explores zero-shot prompting, a technique for interacting with large language models without prior examples, enhancing model output through effective prompt engineering.
- [Few Shot Prompting](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Prompting-Techniques-in-LLM/Few-Shot-Prompting/page.md): Few-shot prompting enhances model responses by providing examples and expected outputs, allowing for flexible and contextually relevant generation.
- [Chain of Thought Prompting](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Prompting-Techniques-in-LLM/Chain-of-Thought-Prompting/page.md): Chain-of-thought prompting instructs models to break down complex tasks into manageable steps, enhancing reasoning and improving response quality in multi-step problems.
- [Adversarial Prompting](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Prompting-Techniques-in-LLM/Adversarial-Prompting/page.md): Adversarial prompting tests language models by crafting inputs to bypass limitations, enhancing prompt engineering for improved model behavior and performance.
- [Inference Parameters](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Prompting-Techniques-in-LLM/Inference-Parameters/page.md): This article explores key inference parameters and their tuning to optimize model behavior during the inference phase of machine learning.
- [Demo Prompting and Parameters](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Prompting-Techniques-in-LLM/Demo-Prompting-and-Parameters/page.md): This article focuses on using Azure AI Studio for accessing and managing generative AI models and exploring various prompting techniques and parameters.
- [Small vs Large Language Models](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Prompting-Techniques-in-LLM/Small-vs-Large-Language-Models/page.md): This article compares small and large language models, discussing their advantages, performance, and suitability for various applications in the context of generative AI.

### Application and Assessment of LLMs

- [Application of LLMs](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Application-and-Assessment-of-LLMs/Application-of-LLMs/page.md): This article explores diverse applications of Large Language Models beyond traditional tasks, including code generation, documentation, and bioinformatics advancements.
- [Assessing LLM Performance](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Application-and-Assessment-of-LLMs/Assessing-LLM-Performance/page.md): Evaluating large language models involves subjective quality assessment, custom metrics, and balancing technical performance with user experience for effective application development.
- [Model Interpretability](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Application-and-Assessment-of-LLMs/Model-Interpretability/page.md): This article discusses the importance of model interpretability in large language models for enhancing transparency, performance, and ethical standards in AI applications.

### Course Introduction

- [Introduction](https://notes.kodekloud.com/docs/Generative-AI-in-Practice-Advanced-Insights-and-Operations/Course-Introduction/Introduction/page.md): This article provides insights into practical applications and advanced concepts of Generative AI, emphasizing hands-on skills and responsible practices for real-world implementation.