This comprehensive workshop guides participants through building a complete contextual chatbot system from scratch, leveraging the power of Lamatic.ai's serverless platform for rapid development and deployment. Participants will learn to create an intelligent chatbot that maintains conversational memory, implements Retrieval-Augmented Generation (RAG), and processes unstructured data through automated ETL pipelines.
Workshop Overview
In this hands-on session, attendees will develop a production-ready agentic chatbot that demonstrates the full spectrum of modern AI capabilities. Using Lamatic.ai's visual flow builder and serverless infrastructure12, participants will construct a system that can understand context, remember previous interactions, and provide intelligent responses based on both its training and real-time data retrieval.
Key Learning Objectives
Serverless Architecture Mastery: Learn to build scalable chatbot systems using Lamatic.ai's serverless platform, which deploys applications with 150ms latency through edge computing3. Participants will understand how serverless architecture eliminates infrastructure management while providing automatic scaling and cost-efficiency45.
Contextual AI Implementation: Develop chatbots that understand and respond based on situational context, user history, and environmental factors67. The workshop covers implementing contextual awareness that enables more natural, human-like interactions by considering past conversations, user preferences, and real-time conditions.
Memory Management Systems: Implement sophisticated memory architectures including short-term session memory, long-term persistent memory, and hybrid systems89. Participants will learn to create chatbots that can maintain conversation context across sessions using techniques like conversation buffer windows and episodic memory summaries10.
Advanced RAG Development: Build comprehensive retrieval-augmented generation systems that combine multiple data sources1112. The workshop covers implementing semantic search, context-aware retrieval, and response generation that leverages both stored knowledge and real-time information.
Technical Components
Lamatic.ai Platform Integration: Utilize Lamatic.ai's visual flow builder, integrated Weaviate vector database, and GraphQL API113. Participants will work with the platform's drag-and-drop interface to create agentic workflows, manage vector embeddings, and deploy serverless applications.
Prompt Engineering Excellence: Master advanced prompting techniques including zero-shot, few-shot, and chain-of-thought prompting1415. Learn to design prompts that guide AI models toward desired outputs while maintaining consistency and reliability across different conversation contexts.
Unstructured ETL Pipeline: Build robust Extract, Transform, Load pipelines for unstructured data using modern techniques1617. The workshop covers processing diverse data formats including PDFs, documents, images, and multimedia content, transforming them into LLM-ready structured formats.
Agentic AI Workflows: Implement autonomous AI agents that can make decisions, adapt to situations, and execute complex tasks1819. Participants will create supervisor agents that orchestrate multiple specialized agents for different aspects of the chatbot functionality.
Hands-on Development
The workshop provides practical experience building each component of the chatbot system. Participants will start with basic conversational flows and progressively add memory management, RAG capabilities, and unstructured data processing. Using Lamatic.ai's no-code/low-code interface2021, attendees can focus on system design and AI logic rather than infrastructure complexities.
Data Integration and Processing: Learn to connect multiple data sources including cloud storage, databases, and real-time APIs22. The workshop covers implementing automated data synchronization, vectorization for semantic search, and maintaining data quality throughout the ETL process.
Testing and Optimization: Develop strategies for testing chatbot performance, memory accuracy, and retrieval effectiveness1. Participants will learn to use Lamatic.ai's built-in testing tools and monitoring capabilities to optimize their systems for production deployment.
Production Deployment
The final component focuses on deploying the complete chatbot system using Lamatic.ai's edge infrastructure2. Participants will learn to configure auto-scaling, implement monitoring and logging, and manage the production lifecycle of their agentic chatbot system.
This workshop combines theoretical understanding with practical implementation, ensuring participants leave with both the knowledge and a working chatbot system they can adapt for their specific use cases. The serverless approach using Lamatic.ai enables rapid prototyping and seamless scaling from development to production environments.