AI Workflow Automation with n8n & LangChainin Los Angeles

Module 1: Introduction to AI Workflow Automation

• Understand workflow automation fundamentals and intelligent process orchestration.
  • Traditional automation vs. AI-powered automation.
  • Business use cases and automation opportunities.
  • Overview of n8n and LangChain capabilities.

Module 2: Getting Started with n8n

• Learn to configure and navigate the n8n automation environment.
  • n8n installation and cloud setup options.
  • Workflow editor, nodes, connections, and executions.
  • Creating and testing your first automated workflow.

Module 3: Workflow Triggers and Control Logic

• Design workflows that respond to events and execute tasks in a defined sequence.
  • Manual, scheduled, and event-based triggers.
  • Conditional branching and switch operations.
  • Loops, data routing, and workflow execution control.

Module 4: API Integration and Data Exchange

• Connect external services and exchange information between applications.
  • REST APIs, HTTP requests, and authentication.
  • JSON structures and response handling.
  • Webhooks and third-party service integration.

Module 5: Data Transformation and Processing

• Prepare and manage data for reliable automated execution.
  • Data mapping, filtering, and field transformation.
  • Expressions and basic JavaScript within n8n.
  • Input validation and structured output formatting.

Module 6: Introduction to Large Language Models

• Understand the AI models that power intelligent workflows.
  • LLM fundamentals and common business applications.
  • Prompt design and response generation.
  • Model selection, limitations, and responsible AI considerations.

Module 7: LangChain Fundamentals

• Explore LangChain components for developing language model applications.
  • Models, prompts, and output parsers.
  • Chains and reusable processing components.
  • Integrating LangChain applications with external services.

Module 8: Connecting n8n with LangChain

• Combine workflow orchestration with language model application logic.
  • Connecting n8n workflows to LangChain services.
  • Passing data between workflow nodes and AI components.
  • Managing API credentials and integration settings.

Module 9: Building AI-Powered Workflows

• Develop automated processes that use LLMs to perform knowledge-based tasks.
  • Text summarisation and content classification.
  • Information extraction and response generation.
  • Structured AI outputs and validation workflows.

Module 10: Retrieval-Augmented Generation (RAG)

• Build workflows that provide AI models with relevant external information.
  • Document ingestion and text chunking.
  • Embeddings and vector database fundamentals.
  • Retrieval, context preparation, and grounded response generation.

Module 11: AI Agents and Tool Integration

• Understand how AI agents use tools to perform multi-step tasks.
  • Agent concepts, tool calling, and task planning.
  • Connecting approved tools and external services.
  • Defining execution limits and reviewing agent outputs.

Module 12: Business Process Automation Use Cases

• Apply AI workflows to common organisational requirements.
  • Customer enquiry classification and response assistance.
  • Document processing and information extraction.
  • Lead management, notifications, and reporting workflows.

Module 13: Error Handling and Workflow Reliability

• Design workflows that can recover from common execution problems.
  • Error triggers, retry strategies, and fallback paths.
  • Input checks, logging, and execution troubleshooting.
  • Handling API failures and unexpected AI responses.

Module 14: Security and Responsible AI Automation

• Apply safeguards to protect data and improve AI workflow reliability.
  • Credential management and access control.
  • Sensitive-data handling and secure integrations.
  • Human approval, output validation, and responsible AI practices.

Module 15: Testing, Monitoring, and Optimisation

• Evaluate workflow behaviour and improve operational performance.
  • Workflow testing and execution history.
  • Monitoring failures, response times, and resource usage.
  • Prompt refinement and workflow performance optimisation.

Module 16: Capstone Project and Practical Implementation

• Build an end-to-end AI workflow using n8n and LangChain.
  • Define a business problem and design the workflow architecture.
  • Integrate APIs, LLM components, and data-processing steps.
  • Test the solution, document results, and demonstrate the completed workflow.

Training Delivery

• Instructor-led learning supported by guided demonstrations and practical exercises.
• Hands-on workflow development, integration tasks, and scenario-based projects.
• Practical guidance for implementing AI workflow automation through StepMerit training in Los Angeles, United States.