Language: English (US)

Design and Integrate AI-Powered S/LLMs into Enterprise Apps using Prompt Engineering, RAG, Fine-Tuning and Vector DBs

https://www.udemy.com/course/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

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.