Intermediate · 6 lessons · about 100 min
Building reliable AI automations
Connect AI to real systems: call APIs safely, receive webhooks, choose an automation platform, get structured output a workflow can trust, ground answers in your own documents and design workflows that survive failures.
By the end you'll be able to
- ✓Call a REST API with credentials from environment variables and handle rate limits and error codes.
- ✓Receive webhooks safely: verify signatures, acknowledge fast and ignore duplicates.
- ✓Choose between n8n, Zapier, Make and GoHighLevel workflows for a given job, and explain the trade-offs.
- ✓Get schema-valid JSON and tool calls from a model, and validate them before they touch a CRM.
- ✓Explain how a RAG pipeline chunks, embeds, retrieves and cites, and design workflows with idempotency, retries, logging and human review.
0 of 6 lessons0%
- ✓1.APIs and API keysLearn how REST APIs work, how authentication and API keys fit in, why keys live in environment variables, and how to behave under rate limits.16m
- ✓2.Webhooks and triggersUnderstand webhooks versus polling, read a real webhook payload, receive webhooks safely and design triggers that fire on the right events.15m
- ✓3.Automation platforms comparedCompare n8n, Zapier, Make and GoHighLevel workflows on billing, hosting, flexibility and who maintains them, and learn when each one fits.14m
- ✓4.Structured output and tool callingGet JSON that matches a schema, let a model call your functions, and validate both before anything touches a real system.18m
- ✓5.RAG fundamentalsGround model answers in your own documents: embeddings, vector databases, chunking, retrieval and cited answers.18m
- ✓6.Reliable workflow architectureDesign automations that survive real-world failures: idempotency, retries with backoff, error handling, logging and human review.18m