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

Name: Generative AI Architectures with LLM, Prompt, RAG, Vector DB
Brand: Udemy

December 14, 2024 Development, Software Engineering, Udemy CoursesUdemy

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$12 ~~$119.99~~ REDEEM

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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

Product Brand: Udemy

Editor's Rating:
4.6

Generative AI Architectures with LLM, Prompt, RAG, Vector DB Coupon. Design and Integrate AI-Powered S/LLMs into Enterprise Apps using Prompt Engineering, RAG, Fine-Tuning and Vector DBs

Created by Mehmet Ozkaya | 6.5 hours on-demand video course | 11 downloadable resources

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

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

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