Launch Yourself Into The Future.
Learn how to create production ready Multi Agent collaborative systems with LLM orchestration and RAG.
Build your own AI Multi Agent System.
AI agents, Language Learning Models (LLMs), and Retrieval-Augmented Generation (RAG) systems are revolutionizing the job market by simulating and modeling complex systems, optimizing and controlling real-world processes, and making better and faster decisions. By learning to build multi-agent systems based on AI orchestration, you will be at the forefront of this transformation.
This is a instructor led, live course. NOT pre recorded Material.
Invest in your future and become a leader in the AI revolution. Join our AI agent course today!
Course Requirements
In order to be successful it is important you have python software development experience and comfortable with loops, and class based programming.
The course will provide you with the knowledge to design, create, and launch a production ready containerised application and facilitate API integration.
Course Takeaways
Multi LLM Framework (rapid dev. and deployment of Ai agents, including the management and fine tunning in a secure env.)
GitHub Repository (reusable code base saving hours of development work)
Foundational Knowledge of LLM applications
A Production Ready Multi Agent LLM RAG application
Access for life to a Live Community of enthousiasts and specialists
What’s Included
16 Instructor led Meetings
14 Source Files
13.5 Hrs of Video Instruction
1 Project Design Document
AI Agents: The Building Blocks of the Future
AI Agents are like smart software programs that can observe what’s happening around them, think about what they see, and then make decisions to reach certain goals. They act as the foundation of more complex systems that can imitate and manage real-world situations.
The Technical Breakdown:
AI Agents operate using advanced algorithms, combining machine learning, decision-making frameworks, and real-time data processing to interact with their environment. They rely on techniques such as reinforcement learning, neural networks, and natural language processing (NLP) to interpret data, adapt to changes, and make independent choices. These agents can communicate with one another, forming multi-agent systems where each agent can coordinate, collaborate, and even compete to solve intricate problems or optimize processes.
Why This Matters for Business
By leveraging AI Agents, businesses can automate complex tasks, improve decision-making accuracy, and enhance operational efficiency. This technology enables organizations to handle tasks that were previously time-consuming or error-prone, leading to faster execution, reduced costs, and the ability to respond quickly to market changes. Ultimately, AI Agents empower businesses to stay ahead in a competitive landscape by delivering smarter solutions that adapt to real-world challenges.
LLM Orchestration: The Conductor of the AI Symphony
LLM (Large Language Model) Orchestration is all about organizing and guiding several AI agents to work in harmony to achieve a shared objective. By carefully managing how these AI agents interact and collaborate, organizations can tackle complex challenges more efficiently and effectively.
The Technical Breakdown
LLM Orchestration involves leveraging large language models to direct the flow of communication, data exchange, and decision-making among multiple AI agents. It integrates advanced natural language processing (NLP) capabilities with multi-agent coordination frameworks, allowing AI agents to understand context, share knowledge, and respond to intricate instructions. This process often includes the use of APIs, message queues, and control algorithms to ensure that each AI agent contributes to the overall goal, adapting in real time based on the evolving situation and input data.
Why This Matters for Business
Implementing LLM Orchestration allows businesses to handle complex, dynamic tasks with greater accuracy and speed. It enables seamless coordination between AI agents, reducing operational silos and enhancing productivity. This leads to more informed decision-making, quicker responses to market changes, and the ability to execute sophisticated strategies that would be difficult for human teams to manage alone, ultimately giving businesses a significant competitive advantage.
Advanced RAG (Retrieval Augmented Generation) Architectures: The AI-Powered Assistant
Advanced RAG (Retrieval Augmented Generation) architectures are AI systems designed to gather, analyze, and create information from different sources, such as text, images, and databases. These architectures enable organizations to develop AI-powered assistants that can aid engineers and architects in quickly building and deploying multi-agent systems to achieve their goals.
The Technical Breakdown
Advanced RAG architectures combine retrieval mechanisms with generative AI models to offer enhanced capabilities. These systems leverage deep learning techniques and knowledge retrieval methods to pull relevant data from large repositories, databases, or external sources, integrating this data into the generation process. By using techniques like attention mechanisms, transformer models, and fine-tuned embeddings, RAG systems can adaptively process unstructured data, generate human-like responses, and seamlessly integrate new information. This capability allows the AI to provide contextually accurate outputs, even when dealing with complex, multi-modal datasets.
Why This Matters for Business
Adopting Advanced RAG architectures means businesses can implement AI-powered assistants that dramatically enhance efficiency, decision-making, and problem-solving. These systems can swiftly access and generate insights from vast data pools, allowing organizations to respond to challenges more effectively and innovate faster. By reducing the time and effort needed to develop AI-driven solutions, RAG architectures help businesses streamline operations, improve productivity, and maintain a competitive edge in an increasingly data-driven world.
Combining AI Technologies and Processes for Competitive Advantage
By integrating AI agents, LLM orchestration, and advanced RAG architectures, organizations can create highly capable and adaptable multi-agent systems that adjust to diverse environments and tasks. These combined technologies empower engineers and architects to develop more effective solutions that adapt to evolving challenges.
The Technical Breakdown
The synergy of AI agents, LLM orchestration, and RAG architectures enables the development of multi-agent systems with enhanced flexibility and functionality. AI agents act autonomously to perceive and interact with their environment, while LLM orchestration ensures smooth coordination and communication between these agents. Meanwhile, advanced RAG architectures provide the capability to retrieve and generate relevant data on demand. This combination creates a cohesive system capable of dynamic decision-making, real-time data processing, and seamless adaptation to changing scenarios, all while maintaining an efficient flow of information between agents.
Why This Matters for Business
Combining these AI technologies allows organizations to simulate and model complex systems with greater accuracy, optimize real-world processes, and make faster, data-driven decisions. This results in streamlined operations, reduced costs, and the ability to respond more effectively to market demands. Ultimately, these advanced multi-agent systems offer a significant competitive edge by enabling businesses to adapt quickly, innovate continuously, and stay ahead in a rapidly changing market landscape.
Course Mondules and Requirements
This course will focus on transfering skills that will support the learner in aquiring Multi Agent, LLMs, and RAG production ready applications. The course is designed to facilitate learning, sharing and 121 guidence and support.
Introduction
Learn how to build and deploy a robust LLM (Large Language Model) and Retrieval-Augmented Generation (RAG) solution with this hands-on, step-by-step course. Perfect for software engineers, architects, and students looking to master AI technologies and create production-ready applications.
Module 1: Define Product Scope and Architecture
1.0 Introduction to Multi Agent systems
1.1 Introduction to LLMs and their limitations
1.2 Introduction to RAG: Bridging the Gap
1.3 Defining Key Components of a RAG System
1.4 Defining Product Scope: Identifying Use Cases
1.5 Designing the Architecture: A Modular Approach
Module 2: Setting Up Development Environment
2.1 Choosing a Platform and Tools
2.2 Configuring the Development Environment
2.3 Data Acquisition and Preparation
Module 3: Setting Up Hosting and CI/CD
3.1 Introduction to Cloud Platforms for Deployment
3.2 Containerization with Docker
3.3 Continuous Integration and Continuous Deployment (CI/CD)
Module 4: Building a Backend Data and AI Platform
4.1 Designing the Data Model for RAG
4.2 Building the Retrieval Component
4.3 Integrating the LLM for Generation
4.4 Building APIs for Data Access and Interaction
4.5. Integrating Symantic Search and Summarisation
Module 5: Integrating Data and Defining Events
5.1 Understanding Event-Driven Architectures
5.2 Building Data Pipelines for Real-Time Updates
5.3 Triggering LLM Actions Based on Events
Module 6: Building Front-End UX, API, and Event Generation
6.1 Designing an Intuitive User Interface
6.2 Building Interactive Components
6.3 Developing a Robust API for Front-End Integration
6.4 Generating Events from User Interactions
Module 7: Building an Adaptive UX
7.1 Understanding User Behavior and Preferences
7.2 Personalizing the User Experience (learning from the user in real time)
7.3 Implementing Adaptive UI Elements
7.4 Integrating a Brain for the Services you use
Module 8: Managing Production Environment
8.1 Deploying to Production
8.2 Monitoring and Logging
8.3 Scaling for Increased Traffic
8.4 Security Best Practices
8.5 Maintaining and Updating Your RAG Solution
Requirements
* If you’re not sure if you fit the requirements reach out! We’re here to help and want to ensure everyone receives the best experience possible!
- Familiarity with software development
- Shell (5%)
- JavaScript (5%)
- Python (90%)
- Front and Back end Python experience:
- loops
- class based software development
- Bonus: Django
- Containerisation using tools like Docker, Kubernetes
- Data Integration (ingestion and processing of data)
- API integrations
Launch Yourself Into The Future.
Become a skilled AI practitioner and create your first AI-powered application. This course equips you with the skills to implement AI technologies confidently, whether you’re aiming for career advancement, entrepreneurship, or simply staying ahead of industry trends
LearnRAG.io offers expert-led courses on AI technologies, specializing in Retrieval-Augmented Generation (RAG), multi-agent systems, and LLM orchestration. Our training empowers professionals and businesses to harness the full potential of AI for smarter decision-making and innovation.