Generative AI Architectures with LLM, Prompt, RAG, Fine-Tuning and Vector DB

I have just published a new course — Generative AI Architectures with LLM, Prompt, RAG, Fine-Tuning and Vector DB

EShop Support App with AI-Powered LLM Capabilities

Get Udemy Course with limited discounted coupon — Generative AI Architectures with LLM, Prompt, RAG, Fine-Tuning and Vector DB

Generative AI is transforming the software industry at an unprecedented pace. The ability to integrate AI capabilities into existing enterprise applications is no longer just a trend — it’s becoming essential for businesses to stay ahead.

In this course, you’ll learn how to Design Generative AI Architectures with integrating AI-Powered S/LLMs into EShop Support Enterprise Applications.

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 and Semantic Search with RAG

Lastly, you’ll learn how to design advanced AI solutions by integrating LLM architectures into enterprise applications. I’ll provide practical, real-world examples so you can design and implement these AI capabilities into your own applications.

Integrating LLM architectures into Enterprise Applications

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This course is structured to cover everything you need to know about building modern AI-powered architectures. We start with the basics of Generative AI and move into an in-depth exploration of LLMs, exploring and running Different LLMs and SLMs: ChatGPT, Llama, Gemini, Phi3.5, Gemma.

Course Flow — A Structured Learning Journey

The learning journey follows the LLM Augmentation Flow:

LLM Augmentation Flow

LLM Augmentation Flow is a powerful framework that uses Prompt Engineering, RAG, Fine-Tuning, and ultimately results in a high-performance trained model. Each topic will have detailed explanations, real-world use cases, and hands-on design examples.

Prompt Engineering

We’ll begin with Prompt Engineering — which is the simplest way to arrange LLM responses by experimenting with different input prompts.

Interacting LLMs with Prompt Engineering

Prompt Engineering is a quick, adaptable approach to get the best responses from LLMs. We’ll design effective prompts to extract accurate responses. Prompt Engineering module cover;

• 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

Retrieval-Augmented Generation (RAG)

Once you understand prompt optimization, we’ll explore RAG (Retrieval-Augmented Generation) — RAG, which allows LLMs to access external data sources and deliver real-time insights.

Retrieval-Augmented Generation (RAG)

RAG integrates external knowledge dynamically. Retrieval-Augmented Generation (RAG) module cover;

• 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)
• Design EShop Customer Support using RAG
• End-to-End RAG Example for EShop Customer Support using OpenAI Playground

Fine-Tuning

Next, we’ll cover Fine-Tuning — a process where you teach the model domain-specific knowledge by training it with your own dataset.

Fine-Tuning for Domain-Specific Use Cases

Fine-Tuning Tailors LLMs to specific domains, enhancing accuracy and depth in specialized tasks. Finally, we’ll integrate everything to produce a fully trained model capable of handling real-world use cases with precision and speed. Fine-Tuning module cover;

• 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

Design Effective Prompts for EShop Support App

After learned all these LLM Augmentation technics, we will start to implement these technics when integrating LLM capabilities with Enterprise Applications. Here you can see image for Developing effective prompts to capture and categorize customer issues.

Design Effective Prompts for EShop Support App

Throughout the course, we’ll design an EShop Customer Support application as a real-world example. This application will showcase how to integrate LLM capabilities such as Prompt Engineering, RAG and Fine-Tuning to automate customer support operations and to develop a robust, AI-powered support system for handling customer queries, providing product information.

Key Project Features:

• Summarization: Condensing customer queries or tickets for quick insights.
• Classification: Automatically categorizing support requests for prioritization.
• Semantic Search: Allowing users to search naturally rather than through rigid keywords.
• Q&A with RAG: Offering precise answers supported by real-time or stored information.

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RAG is Like an Open-Book Exam

We can think RAG as an Open-Book exam, to retrieve up-to-date information from resources.

So, after that we use RAG (Retrieval-Augmented Generation), to access external data sources and deliver real-time insights.

Design EShop Customer Support Using RAG

Here you can see EShop Customer Support application. Integrate real-time data using RAG to ensure responses reflect the latest product information and policies.

Design EShop Customer Support Using RAG

RAG adds real-time data retrieval capabilities, ideal for applications that require up-to-date information.

Design EShop Customer Support Using Fine-Tuning

Here you can see, applying fine-tuning for industry-specific responses,
for EShop Customer Support application ensuring that our AI can handle diverse and unique queries effectively.

You’ll understand how to use these techniques to design, build, and test an end-to-end AI-powered system. I’ll use OpenAI Playground for hands-on implementation, giving you a practical experience with prompts, retrieval, and fine-tuned models.

Choosing the Right Optimization for Enterprise Apps

We’ll explore how to choose the right optimization strategy for your applications.

OpenAI — A Survey of Techniques for Maximizing LLM Performance

Not every scenario requires fine-tuning; sometimes Prompt Engineering or RAG will be sufficient. We’ll teach you how to evaluate your needs and select the most appropriate technique — or combine multiple techniques for the best results.

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Using LLMs and VectorDBs as Cloud-Native Backing Services in Microservices Architecture

With the rise of AI-powered technologies, we’re entering a new era where LLMs and VectorDBs can also function as backing services. These tools enable applications to understand context, generate human-like text, and perform semantic searches, enhancing user experiences and opening up new possibilities.

LLMs and VectorDBs as Cloud-Native Backing Services within Microservices

• Large Language Models (LLMs): Provide language understanding, text generation, summarization, classification, sentiment analysis, and contextual responses.
• Vector Databases (VectorDBs): Enable semantic search, similarity matching, and long-term memory for AI systems by storing high-dimensional vector embeddings.

Designing EShop Support with LLMs, Vector Databases, and Semantic Search

Our goal is to embrace a modern microservices-based architecture that seamlessly integrates AI-powered features.

EShop Support Microservices with LLMs, Vector Databases

1. Microservices with API Gateway

• Client Applications: Web or mobile clients send requests to the system.
• API Gateway: Acts as the single entry point for all client requests.
• Routes requests to appropriate internal microservices.
• We can use the YARP API Gateway library for forwarding requests to internal microservices.

2. CustomerSupport Microservice

• Development Stack: Built with .NET 8.
• Implements the Vertical Slice Architecture for modularity.
• Follows the database-per-service pattern for data isolation.
• Database: Uses PostgreSQL for structured data storage.

3. Cloud-Native AI Backing Services

We introduce AI components as backing services to power our support system.

Ollama in Docker Containers

• Hosts different LLM models like Llama, Gemma, Mistral, Phi-3, etc.
• Provides both LLM functionality and embedding creation.
• Runs Llama 2 for generating context-aware responses.
• Uses all-MiniLM for embedding creation.
• Deployed within Docker containers for scalability and isolation.

Chroma Vector Database

• Stores high-dimensional embeddings. Performs semantic search and similarity matching. Manages chat history. Supports RAG workflows.
• Other vector databases like Weaviate, Pinecone, Qdrant, Milvus.
• PgVector extension if you prefer to stay within PostgreSQL.

4. The Glue Framework — AI Integration Components

To integrate the microservices with AI backing services, we use an AI integration framework.

Semantic Kernel

• Acts as a glue framework for interacting with LLMs.
• Connects the CustomerSupport Microservice to Ollama.
• Facilitates embedding generation. Manages prompts and responses.

Containerized Deployment

• All services, including LLMs, databases, and microservices, are containerized using Docker.
• They run within a unified Docker network, ensuring robust communication and scalability.

Designing EShop Support with Azure Cloud AI Services: Azure OpenAI and Azure AI Search

By harnessing the capabilities of Azure OpenAI and Azure AI Search, we’ve designed an intelligent, cloud-native support system that elevates the customer experience that demonstrates how enterprises can modernize their support systems using AI-powered capabilities.

EShop Support Microservices with Azure Cloud AI Services

1. LLMs as Backing Services: Azure OpenAI GPT-4 serves as the brain of our application, generating intelligent and context-aware responses. Enables our support agents to handle customer inquiries more effectively.
2. Vector Databases as Backing Services: Azure AI Search functions as our semantic memory, storing embeddings and enabling rapid, relevant information retrieval. Enhances search capabilities beyond traditional keyword matching.
3. Cloud-Native Architecture: Utilizing Azure’s managed services simplifies deployment and ensures scalability. Provides a robust infrastructure that can grow with our business needs.

Get Udemy Course with limited discounted coupon — Generative AI Architectures with LLM, Prompt, RAG, Fine-Tuning and Vector DB

Enroll Now and Start Building AI-Powered Systems

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.

EShop Support App with AI-Powered LLM Capabilities

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.

Get Udemy Course with limited discounted coupon — Generative AI Architectures with LLM, Prompt, RAG, Fine-Tuning and Vector DB