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via Udemy |
Go to Course: https://www.udemy.com/course/generative-ai-architectures-with-llm-prompt-rag-vector-db/
Certainly! Here's a comprehensive review and recommendation for the Coursera course on Designing Generative AI Architectures: --- **Course Review and Recommendation: Designing Generative AI Architectures for EShop Support Enterprise Applications** If you're looking to master the art of integrating Generative AI into enterprise applications, particularly in e-commerce support, this Coursera course is an exceptional choice. It offers an in-depth, hands-on approach to designing sophisticated AI architectures that leverage the latest advancements in Large Language Models (LLMs), Retrieval Augmented Generation (RAG), fine-tuning, and vector databases. **What You'll Learn:** - **Foundation of Large Language Models (LLMs):** Understanding how models like ChatGPT, Llama, Anthropic Claude, Google Gemini, and others work, their capabilities in text generation, summarization, Q&A, classification, sentiment analysis, semantic search, and code generation. - **Prompt Engineering Techniques:** Crafting effective prompts using zero-shot, few-shot, chain-of-thought, and role-based prompts to optimize LLM results for specific tasks like customer support, ticket classification, and sentiment analysis. - **Retrieval-Augmented Generation (RAG):** Designing architectures that combine embeddings, vector search, reranking, and context queries to create responsive, accurate AI-powered support systems. - **Fine-Tuning:** Exploring various methods like full fine-tuning, Parameter-Efficient Fine-Tuning (PEFT), LoRA, and transfer learning to customize models for enterprise needs. - **Vector Databases and Semantic Search:** Understanding vector embeddings, similarity metrics, and working with databases like Pinecone, Chroma, Weaviate, Qdrant, Milvus, and Redis to implement efficient semantic search capabilities. - **End-to-End System Design:** Building comprehensive architectures integrating LLMs and vector databases within cloud-native, microservices environments, especially using Azure AI services. **Strengths:** - **Progressive Learning Path:** The course starts from foundational concepts and builds up to complex architectures, making it suitable for both beginners and advanced practitioners. - **Hands-On Projects:** Practical experience is emphasized, with activities involving running local models, designing prompts, implementing RAG workflows, and fine-tuning models using platforms like OpenAI Playground and Ollama. - **Real-World Application:** Focused on e-commerce support systems, the course provides concrete examples such as customer support chatbots, ticket processing, and semantic search for enterprise solutions. - **Cutting-Edge Content:** Covers the latest models and tools, ensuring learners stay current with industry trends. **Who Should Enroll:** - AI/ML practitioners interested in enterprise AI solutions - Software engineers and architects designing AI-driven customer support systems - Data scientists focusing on NLP, retrieval systems, and model fine-tuning - Tech leads and product managers aiming to incorporate advanced AI capabilities into their applications **Final Verdict:** This course is a comprehensive, well-structured package that balances theoretical knowledge with practical application. Its focus on real-world enterprise use cases, especially for e-commerce support, makes it highly relevant for professionals aiming to innovate in customer service using Generative AI. **Recommendation:** If you're ready to deepen your understanding of how to leverage LLMs, RAG, fine-tuning, and vector databases to build intelligent, scalable support applications, this course is highly recommended. It not only equips you with essential technical skills but also guides you through designing and deploying end-to-end AI solutions tailored to enterprise needs. --- Feel free to ask if you'd like a more personalized summary or additional insights!
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 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 CapabilitiesPrompt Engineering module;Steps of Designing Effective Prompts: Iterate, Evaluate and TemplatizeAdvanced Prompting Techniques: Zero-shot, One-shot, Few-shot, Chain-of-Thought, Instruction and Role-basedDesign 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 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 PlaygroundFine-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 - 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 Customer Support application, including LLM capabilities like Summarization, Q & A, Classification, Sentiment Analysis, Embedding Semantic Search, Code Generation.