Developing Advanced LLM Applications Training

This advanced Generative AI training is designed for developers who want to explore enterprise-grade Large Language Model (LLM) architectures and design patterns. This course covers chatbot architectures, Agentic RAG, LLM-powered agents, and model serving and deployment techniques. Participants learn how to design and implement advanced LLM-based applications using cutting-edge technologies and frameworks.

Course Details


4 days


  • Practical programming skills in Python and familiarity with LLM concepts and frameworks (3+ Months LLM, 6+ Months Python and Machine Learning)
    • LLM Access via API, Open Source Libraries (HuggingFace)
    • LLM Application development experience (RAG, Chatbots, etc)
  • Familiarity with deep learning concepts and frameworks (e.g., TensorFlow, PyTorch)
  • Experience with software development practices, system design, and enterprise application architecture recommended
  • CI/CD Pipelines and monitoring for traditional ML models (MLOps) recommended

Skills Gained

  • Design and implement advanced chatbot architectures using LLMs and enterprise system integration
  • Implement advanced Agentic RAG architectures and techniques for complex reasoning and knowledge retrieval
  • Design and implement LLM-powered agents and multi-agent workflows for autonomous decision-making and task completion
  • Apply advanced model serving and deployment techniques, including CI/CD pipelines and monitoring
Course Outline
  • Deep Dive into Enterprise-Grade Chatbot Architectures
    • Designing and implementing advanced chatbot architectures using LLMs
      • Leveraging multi-turn conversation management and context tracking techniques
      • Implementing personalized and adaptive chatbot interactions based on user profiles
    • Integrating chatbots with enterprise systems and workflows
      • Strategies for integrating chatbots with CRM, ERP, and other enterprise applications
      • Implementing secure authentication and authorization mechanisms for chatbot interactions
    • Building an enterprise-grade chatbot using advanced LLM architectures
      • Designing and implementing a multi-turn, context-aware chatbot architecture
      • Integrating the chatbot with enterprise systems and implementing security measures
  • Advanced Agentic RAG Architectures and Techniques
    • Exploring advanced Agentic RAG architectures and design patterns
      • Implementing multi-hop reasoning and iterative query refinement techniques in RAG
      • Leveraging graph-based knowledge representations and reasoning in Agentic RAG
    • Optimizing Agentic RAG performance and scalability
      • Implementing distributed retrieval and generation techniques for large-scale Agentic RAG
      • Leveraging caching, pruning, and other optimization techniques for efficient Agentic RAG inference
    • Implementing an advanced Agentic RAG architecture for a specific use case
      • Designing and implementing a multi-hop Agentic RAG architecture with graph-based reasoning
      • Optimizing the Agentic RAG implementation for performance and scalability
  • Designing and Implementing LLM-Powered Agents and Workflows
    • Designing LLM-powered agents for autonomous decision-making and task completion
      • Implementing goal-oriented and adaptive agent architectures using LLMs
      • Leveraging reinforcement learning and planning techniques for agent decision-making
    • Orchestrating multi-agent workflows and interactions in enterprise environments
      • Designing and implementing multi-agent communication and coordination protocols
      • Implementing fault-tolerant and scalable multi-agent workflows using serverless architectures
    • Building an LLM-powered agent-based workflow for a specific enterprise use case
      • Designing and implementing a goal-oriented, adaptive agent architecture using LLMs
      • Orchestrating a multi-agent workflow using serverless technologies and coordination protocols
  • Advanced Model Serving and Deployment Techniques
    • Exploring advanced model serving architectures and design patterns
      • Implementing model versioning, A/B testing
      • Leveraging serverless and edge computing for low-latency and cost-efficient model serving
    • Implementing CI/CD pipelines for automated model deployment and monitoring
      • Designing and implementing end-to-end CI/CD pipelines for LLM-based applications
      • Integrating model performance monitoring and drift detection into CI/CD workflows
    • Implementing an advanced model serving architecture with CI/CD for an LLM-based application
      • Designing and implementing a serverless model serving architecture with versioning and A/B testing
    • Setting up a CI/CD pipeline for automated model deployment and monitoring