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

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 toDesign Generative AI Architectureswithintegrating AI-Powered S/LLMsintoEShop Support Enterprise Applicationsusing 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

Westart withthebasicsandprogressivelydive deeperinto each topic. We'll also followLLM Augmentation Flowis 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 ismore than just learning Generative AI, it's a deep dive into the world ofhow todesign Advanced AIsolutions byintegrating LLM architecturesintoEnterprise applications.

You'll gethands-on experiencedesigning a completeEShop application, including LLM capabilities like Summarization, Q&A, Classification, Sentiment Analysis, Embedding Semantic Search, Code Generation.

Who this course is for:
- Beginner to integrate AI-Powered LLMs into Enterprise Apps
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