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

Date: 30 September 2025 | Posted by: voska89

Free Download Generative AI Architectures with LLM, Prompt, RAG, Vector DB
Last updated 9/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.96 GB | Duration: 7h 19m
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
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
Requirements
Basics of Software Developments
Description
In this course, you'll learn how to Design Generative AIÂ 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 CapabilitiesDevelop .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 TemplatizeAdvanced Prompting Techniques:Â Zero-shot, One-shot, Few-shot, Chain-of-Thought, Instruction and Role-basedDesign Advanced Prompts for EShop Support â 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 PlaygroundDevelop RAG â Retrieval-Augmented Generation with .NET, implement the full RAG flow with real examples using .NETFine-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 PlaygroundAlso, we will discussChoosing the Right Optimization â Prompt Engineering, RAG, and Fine-TuningVector Database and Semantic Search with RAG moduleWhat 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, RedisLastly, we will Design EShopSupport Architecture with LLMs and Vector DatabasesUsing 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 SearchThis 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.
Beginner to integrate AI-Powered LLMs into Enterprise Apps
Homepage
https://www.udemy.com/course/generative-ai-architectures-with-llm-prompt-rag-vector-db/

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