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

Preview this Course

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

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 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

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

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

Lastly, we will discuss

Choosing the Right Optimization – Prompt Engineering, RAG, and Fine-Tuning

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 Customer Support application, including LLM capabilities like Summarization, Q&A, Classification, Sentiment Analysis, Embedding Semantic Search, Code Generation.