Developing Advanced LLM Applications

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

Conclusion