Connect OpenAI, Claude, Gemini, and Ollama to n8n and route each AI model to the right task in your automation workflow

Write effective AI prompts using live data inputs and control model behaviour with temperature and token settings

Make AI return structured, usable data and build workflows that stay reliable using validation and fallback logic

Build and test a real dual-model AI Email Intelligence System using OpenAI and Claude with Notion integration

Understand how AI agents think and decide, when to use them over an LLM call, and how to connect language models to your n8n agent workflows

Teach your agents to use tools, retain memory across tasks, and make decisions on their own so they can handle complex work without constant input

Learn how APIs and webhooks work from scratch, then connect live data sources, external services, and custom code logic to your n8n automations

Make your integrations production-ready with HMAC-based security, rate limiting, and protection against unauthorized access and replay attacks

Understand why AI agents lose context and how AI memory systems improve response accuracy and relevance

Build production-ready RAG workflows that connect AI applications to enterprise documents and knowledge

Implement semantic search, retrieval, fallback handling, and debugging techniques for reliable AI outputs

Create multimodal AI workflows in n8n that retrieve information from both documents and images

Understand MCP fundamentals and enable secure AI access to workflows, tools, and business systems

Connect AI applications to n8n workflows and manage secure tool access through MCP

Build AI-powered automation workflows that integrate data sources, business tools, and external services

Design reliable AI systems with orchestration, security, debugging, failure handling, and cost controls

Understand how multi-agent AI systems coordinate tasks, share context, and support business workflows

Build AI systems that retrieve accurate answers from enterprise knowledge using RAG workflows

Implement AI classification, response generation, routing, and human approval workflows for customer support

Test, optimize, and operate reliable AI systems using production-ready monitoring and quality practices

Deploy, test, andvalidateAI workflows to ensure reliable performance before production release

OptimizeAI workflows, handle uncertain responses, and migrate solutions across environments

Monitor, troubleshoot, and improve AI systems using logs, alerts, cost tracking, and debugging practices

Strengthen AI security, evaluate response quality, and apply governance controls for production systems