Generative AI Architectures with LLM, Prompt, RAG, Vector DB

Descripción

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 Engineering • Retrieval Augmented Generation (RAG) • Fine-Tuning • Vector Databases We 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 Generation • Generate 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 Grok • SLM Models: OpenAI ChatGPT 4o mini, Meta Llama 3.2 mini, Google Gemma, Microsoft Phi 3.5 • Interacting Different LLMs with Chat UI: ChatGPT, LLama, Mixtral, Phi3 • Interacting OpenAI Chat Completions Endpoint with Coding • Installing and Running Llama and Gemma Models Using Ollama to run LLMs locally • Modernizing and Design EShop Support Enterprise Apps with AI-Powered LLM Capabilities • Develop .NET to integrate LLM models and performs Classification, Summarization, Data extraction, Anomaly detection, Translation and Sentiment Analysis use cases. Prompt Engineering module; • Steps of Designing Effective Prompts: Iterate, Evaluate and Templatize • Advanced Prompting Techniques: Zero-shot, One-shot, Few-shot, Chain-of-Thought, Instruction and Role-based • Design 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 RAG Retrieval-Augmented Generation (RAG) module; • The RAG Architecture Part 1: Ingestion with Embeddings and Vector Search • The RAG Architecture Part 2: Retrieval with Reranking and Context Query Prompts • The RAG Architecture Part 3: Generation with Generator and Output • E2E Workflow of a Retrieval-Augmented Generation (RAG) - The RAG Workflow • Design EShop Customer Support using RAG • End-to-End RAG Example for EShop Customer Support using OpenAI Playground • Develop RAG – Retrieval-Augmented Generation with .NET, implement the full RAG flow with real examples using .NET Fine-Tuning module; • Fine-Tuning Workflow • Fine-Tuning Methods: Full, Parameter-Efficient Fine-Tuning (PEFT), LoRA, Transfer • Design EShop Customer Support Using Fine-Tuning • End-to-End Fine-Tuning a LLM for EShop Customer Support using OpenAI Playground Also, we will discuss • Choosing the Right Optimization – Prompt Engineering, RAG, and Fine-Tuning Vector Database and Semantic Search with RAG module • What are Vectors, Vector Embeddings and Vector Database? • Explore Vector Embedding Models: OpenAI - text-embedding-3-small, Ollama - all-minilm • Semantic Meaning and Similarity Search: Cosine Similarity, Euclidean Distance • How Vector Databases Work: Vector Creation, Indexing, Search • Vector Search Algorithms: kNN, ANN, and Disk-ANN • Explore Vector Databases: Pinecone, Chroma, Weaviate, Qdrant, Milvus, PgVector, Redis Lastly, we will Design EShopSupport Architecture with LLMs and Vector Databases • Using LLMs and VectorDBs as Cloud-Native Backing Services in Microservices Architecture • Design EShop Support with LLMs, Vector Databases and Semantic Search • Azure Cloud AI Services: Azure OpenAI, Azure AI Search • Design EShop Support with Azure Cloud AI Services: Azure OpenAI, Azure AI Search This 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 application, including LLM capabilities like Summarization, Q&A, Classification, Sentiment Analysis, Embedding Semantic Search, Code Generation.

Lo que aprenderás

• Generative AI Model Architectures (Types of Generative AI Models)
• Transformer Architecture: Attention is All you Need
• Large Language Models (LLMs) Architectures
• Text Generation, Summarization, Q&A, Classification, Sentiment Analysis, Embedding Semantic Search
• 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
• Vector Database and Semantic Search with RAG
• Explore Vector Embedding Models: OpenAI - text-embedding-3-small, Ollama - all-minilm
• Explore Vector Databases: Pinecone, Chroma, Weaviate, Qdrant, Milvus, PgVector, Redis
• Using LLMs and VectorDBs as Cloud-Native Backing Services in Microservices Architecture
• Design EShop Support with LLMs, Vector Databases and Semantic Search
• Design EShop Support with Azure Cloud AI Services: Azure OpenAI, Azure AI Search
• Develop .NET to integrate LLM models and performs Classification, Summarization, Data extraction, Anomaly detection, Translation and Sentiment Analysis use case
• Develop RAG – Retrieval-Augmented Generation with .NET, implement the full RAG flow with real examples using .NET and Qdrant

Requisitos

• Basics of Software Developments