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Generative AI Architectures with LLM Prompt RAG Vector DB - OneDDL - 11-24-2024 Free Download Generative AI Architectures with LLM Prompt RAG Vector DB Published 11/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.04 GB | Duration: 5h 27m Design and Integrate AI-Powered S/LLMs into Enterprise Apps using Prompt Engineering, RAG, Fine-Tuning and Vector DBs What you'll learn Generative AI Model Architectures (Types of Generative AI Models) Transformer Architecture: Attention is All you Need Large Language Models (LLMs) Architectures Capabilities of LLMs: Text Generation, Summarization, Q&A, Classification, Sentiment Analysis, Embedding Semantic Search, Code Generation Generate Text with ChatGPT: Understand Capabilities and Limitations of LLMs (Hands-on) Function Calling and Structured Outputs in Large Language Models (LLMs) LLM Providers: OpenAI, Meta AI, Anthropic, Hugging Face, Microsoft, Google and Mistral AI LLM Models: OpenAI ChatGPT, Meta Llama, Anthropic Claude, Google Gemini, Mistral Mixral, xAI Grok SLM Models: OpenAI ChatGPT 4o mini, Meta Llama 3.2 mini, Google Gemma, Microsoft Phi 3.5 How to Choose LLM Models: Quality, Speed, Price, Latency and Context Window Interacting Different LLMs with Chat UI: ChatGPT, LLama, Mixtral, Phi3 Installing and Running Llama and Gemma Models Using Ollama Modernizing Enterprise Apps with AI-Powered LLM Capabilities Designing the 'EShop Support App' with AI-Powered LLM Capabilities Advanced Prompting Techniques: Zero-shot, One-shot, Few-shot, COT Design Advanced Prompts for Ticket Detail Page in EShop Support App w/ Q&A Chat and RAG The RAG Architecture: Ingestion with Embeddings and Vector Search E2E Workflow of a Retrieval-Augmented Generation (RAG) - The RAG Workflow End-to-End RAG Example for EShop Customer Support using OpenAI Playground Fine-Tuning Methods: Full, Parameter-Efficient Fine-Tuning (PEFT), LoRA, Transfer End-to-End Fine-Tuning a LLM for EShop Customer Support using OpenAI Playground Choosing the Right Optimization - Prompt Engineering, RAG, and Fine-Tuning Requirements Basics of Software Architectures Description In this course, you'll learn how to Design Generative AI Architectures with integrating AI-Powered S/LLMs into EShop Support Enterprise Applications using Prompt Engineering, RAG, Fine-tuning and Vector DBs.We will design Generative AI Architectures with below components;Small and Large Language Models (S/LLMs)Prompt EngineeringRetrieval Augmented Generation (RAG)Fine-TuningVector DatabasesWe start with the basics and progressively dive deeper into each topic. We'll also follow LLM Augmentation Flow is a powerful framework that augments LLM results following the Prompt Engineering, RAG and Fine-Tuning.Large Language Models (LLMs) module;How Large Language Models (LLMs) works?Capabilities of LLMs: Text Generation, Summarization, Q&A, Classification, Sentiment Analysis, Embedding Semantic Search, Code GenerationGenerate Text with ChatGPT: Understand Capabilities and Limitations of LLMs (Hands-on)Function Calling and Structured Output in Large Language Models (LLMs)LLM Models: OpenAI ChatGPT, Meta Llama, Anthropic Claude, Google Gemini, Mistral Mixral, xAI GrokSLM Models: OpenAI ChatGPT 4o mini, Meta Llama 3.2 mini, Google Gemma, Microsoft Phi 3.5Interacting Different LLMs with Chat UI: ChatGPT, LLama, Mixtral, Phi3Interacting OpenAI Chat Completions Endpoint with CodingInstalling and Running Llama and Gemma Models Using Ollama to run LLMs locallyModernizing and Design EShop Support Enterprise Apps with AI-Powered LLM CapabilitiesPrompt Engineering module;Steps of Designing Effective Prompts: Iterate, Evaluate and TemplatizeAdvanced Prompting Techniques: Zero-shot, One-shot, Few-shot, Chain-of-Thought, Instruction and Role-basedDesign Advanced Prompts for EShop Support - Classification, Sentiment Analysis, Summarization, Q&A Chat, and Response Text Generation Design Advanced Prompts for Ticket Detail Page in EShop Support App w/ Q&A Chat and RAGRetrieval-Augmented Generation (RAG) module;The RAG Architecture Part 1: Ingestion with Embeddings and Vector SearchThe RAG Architecture Part 2: Retrieval with Reranking and Context Query PromptsThe RAG Architecture Part 3: Generation with Generator and OutputE2E Workflow of a Retrieval-Augmented Generation (RAG) - The RAG WorkflowDesign EShop Customer Support using RAGEnd-to-End RAG Example for EShop Customer Support using OpenAI PlaygroundFine-Tuning module;Fine-Tuning WorkflowFine-Tuning Methods: Full, Parameter-Efficient Fine-Tuning (PEFT), LoRA, TransferDesign EShop Customer Support Using Fine-TuningEnd-to-End Fine-Tuning a LLM for EShop Customer Support using OpenAI PlaygroundLastly, we will discussChoosing the Right Optimization - Prompt Engineering, RAG, and Fine-TuningThis course is more than just learning Generative AI, it's a deep dive into the world of how to design Advanced AI solutions by integrating LLM architectures into Enterprise applications. You'll get hands-on experience designing a complete EShop Customer Support application, including LLM capabilities like Summarization, Q&A, Classification, Sentiment Analysis, Embedding Semantic Search, Code Generation. Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 Tools and Resources for the Course - Course Slides Lecture 3 Course Project: EShop Customer Support with AI-Powered Capabilities using LLMs Section 2: What is Generative AI ? Lecture 4 Evolution of AI: AI, Machine Learning, Deep Learning and Generative AI Lecture 5 What is Generative AI ? Lecture 6 How Generative AI works ? Lecture 7 Generative AI Model Architectures (Types of Generative AI Models) Lecture 8 Transformer Architecture: Attention is All you Need Section 3: What are Large Language Models (LLMs) ? Lecture 9 What are Large Language Models (LLMs) ? Lecture 10 How Large Language Models (LLMs) works? Lecture 11 What is Token And Tokenization ? Lecture 12 How LLMs Use Tokens Lecture 13 Capabilities of LLMs: Text Generation, Summarization, Q&A, Classification Lecture 14 LLM Use Cases and Real-World Applications Lecture 15 Limitations of Large Language Models (LLMs) Lecture 16 Generate Text with ChatGPT: Understand Capabilities and Limitations of LLMs Lecture 17 LLM Settings: Temperature, Max Tokens, Stop sequences, Top P, Frequency Penalty Lecture 18 Function Calling in Large Language Models (LLMs) Lecture 19 Structured Output in Large Language Models (LLMs) Lecture 20 What are Small Language Models (SLMs) ? Use Cases / How / Why / When Section 4: Exploring and Running Different LLMs w/ HuggingFace and Ollama Lecture 21 LLM Providers: OpenAI, Meta AI, Anthropic, Hugging Face, Microsoft, Google Lecture 22 LLM Models: OpenAI ChatGPT, Meta Llama, Anthropic Claude, Google Gemini, Mistral Lecture 23 SLM Models: OpenAI ChatGPT 4o mini, Meta Llama 3.2 mini, Gemma, Phi-3 Lecture 24 How to Choose LLM Models: Quality, Speed, Price, Latency and Context Window Lecture 25 Open Source vs Proprietary Models Lecture 26 Hugging Face - The GitHub of Machine Learning Models Lecture 27 LLM Interaction Types: No-Code (ChatUI) or With-Code (API Keys) Lecture 28 Interacting Different LLMs with Chat UI: ChatGPT, LLama, Mixtral, Phi3 Lecture 29 Interacting OpenAI Chat Completions Endpoint with Coding Lecture 30 Ollama - Run LLMs Locally Lecture 31 Installing and Running Llama and Gemma Models Using Ollama Lecture 32 Ollama integration using Semantic Kernel and C# with coding Lecture 33 Modernizing Enterprise Apps with AI-Powered LLM Capabilities Lecture 34 Designing the 'EShop Support App' with AI-Powered LLM Capabilities Lecture 35 LLMs Augmentation Flow: Prompt Engineering -> RAG -> Fine tunning -> Trained Section 5: Prompt Engineering Lecture 36 What is Prompt ? Lecture 37 Elements and Roles of a Prompt Lecture 38 What is Prompt Engineering ? Lecture 39 Steps of Designing Effective Prompts: Iterate, Evaluate and Templatize Lecture 40 Advanced Prompting Techniques Lecture 41 Zero-Shot Prompting Lecture 42 One-shot Prompting Lecture 43 Few-shot Prompting Lecture 44 Chain-of-Thought Prompting Lecture 45 Instruction-based and Role-based Prompting Lecture 46 Design Advanced Prompts for EShop Support - Classification, Sentiment Analysis Lecture 47 Design Advanced Prompts for Ticket Detail Page in EShop Support App w/ Q&A Chat Lecture 48 Test Prompts for Eshop Support Customer Ticket w/ Playground Section 6: Retrieval-Augmented Generation (RAG) Lecture 49 What is Retrieval-Augmented Generation (RAG) ? Lecture 50 Why Need Retrieval-Augmented Generation (RAG) ? Why is RAG Important? Lecture 51 How Does Retrieval-Augmented Generation (RAG) Work? Lecture 52 The RAG Architecture Part 1: Ingestion with Embeddings and Vector Search Lecture 53 The RAG Architecture Part 2: Retrieval with Reranking and Context Query Prompts Lecture 54 The RAG Architecture Part 3: Generation with Generator and Output Lecture 55 E2E Workflow of a Retrieval-Augmented Generation (RAG) - The RAG Workflow Lecture 56 Applications Use Cases of RAG Lecture 57 Challenges and Key Considerations of Using RAG -- Retrieval-Augmented Generation Lecture 58 Design EShop Customer Support using RAG Lecture 59 End-to-End RAG Example for EShop Customer Support using OpenAI Playground Section 7: Fine-Tuning LLMs Lecture 60 What is Fine-Tuning ? Lecture 61 Why Need Fine-Tuning ? Lecture 62 When to Use Fine-Tuning ? Lecture 63 How Does Fine-Tuning Work? Lecture 64 Fine-Tuning Methods: Full, Parameter-Efficient Fine-Tuning (PEFT), LoRA Lecture 65 Applications & Use Cases of Fine-Tuning Lecture 66 Challenges and Key Considerations of Fine-Tuning Lecture 67 Design EShop Customer Support Using Fine-Tuning Lecture 68 End-to-End Fine-Tuning a LLM for EShop Customer Support using OpenAI Playground Section 8: Choosing the Right Optimization - Prompt Engineering, RAG, and Fine-Tuning Lecture 69 Comparison of Prompt Engineering, RAG, and Fine-Tuning Lecture 70 Choosing the Right Optimization - Prompt Engineering, RAG, and Fine-Tuning Lecture 71 Training Own Model for LLM Optimization Lecture 72 Thanks Beginner to integrate AI-Powered LLMs into Enterprise Apps Homepage [To see links please register or login] Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [To see links please register or login] No Password - Links are Interchangeable