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

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; 1. Small and Large Language Models (S/LLMs) 2. Prompt Engineering 3. Retrieval Augmented Generation (RAG) 4. Fine-Tuning 5. 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.

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
• 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