About the Course
You've learned to use AI, but you're still doing the manual glue work—copying, pasting, switching between tools, running the same prompts every week. This course closes that gap. AI Workflows teaches you to build automations where AI is one step in a larger pipeline, handling routine tasks while you focus on what actually matters. Over 2–3 weeks, you'll move from "I prompt AI manually" to "My workflows run on their own," with working automations you've built yourself for the repetitive work already clogging your week. You'll understand when to automate (and when not to), connect tools that don't naturally integrate, add AI as a thinking step in your pipelines, and diagnose what broke before you even notice something's wrong. This is the bridge between using AI as a chatbot and deploying it as infrastructure.
Who this course is for
Who They Are
Our students are people who've learned to use AI but haven't yet made it do anything on its own.
They finished an intro course, or figured things out themselves. They use ChatGPT or Claude regularly. They know how to prompt. But every time they want AI to help them, they have to go start a conversation, paste something in, copy something out, and do it all again tomorrow. The magic hasn't compounded. AI is a tool they pick up and put down — not a system that works for them.
They want that to change. They've heard about automation. They've maybe opened Zapier once and closed it. They sense there's a level between "chatting with AI" and "running my own server," and they want to find it.
They may be: - IntroToAI graduates who got the job (or kept it) and now want to go further - Professionals who use AI tools daily but are still doing the repetitive glue work themselves - Small business owners or freelancers drowning in operational overhead they suspect could be automated - People who've tried no-code automation tools before but never connected them to AI in any meaningful way - Anyone who's ever thought: "I do this exact same thing every Monday — why am I still doing it manually?"
What They're Feeling
The Frustration of Half-Automation
- They've gotten good at prompting but the results still require too much manual effort to be truly useful
- They copy-paste between tools constantly — browser to AI to spreadsheet to email — and it's exhausting
- They watch people talk about "AI workflows" and nod along without quite knowing what that means in practice
- The tools exist. They just haven't figured out how to connect them.
Excitement Tempered by Confusion
- The promise of automation is real and they can feel it — a few tasks fully handled while they focus on something else
- But the landscape is overwhelming: Zapier, Make, n8n, IFTTT, APIs, webhooks — where do you even start?
- They don't know whether they need to learn to code, and the uncertainty is paralyzing
- They've started tutorials that assumed too much and abandoned them
Readiness
- Unlike complete beginners, these students aren't afraid of AI — they're comfortable with it
- They're ready to invest real time in setting something up properly if they know it'll actually work at the end
- They learn by building, not by watching
- They're motivated by the specific tasks they already know they want to automate
What's At Stake
The difference between an AI user and an AI workflow builder isn't complexity — it's compounding:
| Approach | Daily Reality |
|---|---|
| AI user | Opens a tool, prompts it, gets a result, does something with that result manually. Repeated every time. |
| Workflow builder | Triggers fire automatically. Data moves between tools. Results land where they need to be. Time reclaimed. |
The student who finishes this course doesn't work harder with AI — they work once and let the workflow work for them.
Their Starting Point
- Technical level: Comfortable with web tools, SaaS apps, and the concept of connecting them. Has used tools like Google Sheets, Notion, Gmail, Slack in a professional context. No coding required or expected.
- AI exposure: Has used AI conversationally and gotten real value from it. Understands prompting at a basic level. Knows what an LLM is and what it can and can't do.
- Automation exposure: Minimal to none. May have heard of Zapier or tried it briefly. Understands the concept of "if this then that" but hasn't built anything meaningful.
- Learning style: Hands-on. Needs to see a workflow run before they trust it. Learns by modifying examples, not reading documentation.
- Time horizon: Can commit 2–3 weeks part-time. Willing to invest setup time if the payoff is clear and real. Not interested in toy examples.
- Emotional state: Impatient with theory, energized by working demos.
Desired Outcomes
By completing this course, students will have:
1. A Working Mental Model
- Understand what automation actually is: triggers, actions, data passing, and logic
- Know the difference between tools (Zapier vs. Make vs. n8n) and when each makes sense
- Be able to look at a repetitive task in their life and see the workflow hiding inside it
- Understand where AI fits in a workflow vs. where it doesn't add value
2. Real, Running Automations
- At least three working workflows doing useful things in their actual life or work
- An AI-enhanced workflow that uses an LLM as a step — not just as the destination
- A workflow they built themselves, not just followed along with
3. The Skill to Build More
- Confidence to design a new workflow from scratch when the next need arises
- Ability to debug a broken automation without panicking
- Understanding of APIs and webhooks at the level needed to connect tools that don't have native integrations
- A workflow library they can adapt rather than starting from zero each time
4. A Bridge to Deeper Capability
- Clear understanding of what an automation tool can do vs. what a full autonomous agent can do
- Awareness of when they've outgrown no-code automation and what the next step looks like (AgenticAI)
- A running setup that can serve as the foundation for more sophisticated agentic work later
Success Metrics
We'll know the course works when students can:
- [ ] Explain what a trigger, action, and data mapping are — without jargon
- [ ] Build a three-step workflow from scratch in Make or n8n
- [ ] Insert an AI step (LLM call) into a workflow and pass data in and out of it correctly
- [ ] Connect two tools that don't have a native integration using webhooks
- [ ] Look at a broken workflow, identify where it failed, and fix it
- [ ] Identify a repetitive task in their own work and design a workflow to handle it
- [ ] Articulate what automation can't do — and why an agent might be needed instead
The Promise
You've already learned to talk to AI. Now learn to put it to work.
This course is the bridge between "I use AI tools" and "I run an AI-powered operation." No coding. No servers. Just workflows that run while you do something more important.
Key Goals
After completing this course, you will be able to:
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Identify and map automatable tasks — Distinguish between decisions (which need you) and procedures (which don't), and recognize which 25–75% of your weekly work can run without your participation.
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Build a complete, working multi-step workflow from scratch — Set up triggers, configure actions, map data between steps, and deploy an automation that runs reliably on its own schedule.
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Add an LLM as a processing step in a workflow — Connect an AI API to your automation platform, write prompts that work unattended, and extract structured data that downstream steps can reliably use.
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Connect tools that lack native integrations using webhooks and APIs — Understand REST APIs at a practical level, make HTTP calls from within workflows, and integrate tools that would otherwise require custom code.
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Debug a broken automation and prevent future failures — Diagnose where a workflow failed, understand error handling patterns, set up monitoring and alerts, and design automations that tell you when something goes wrong.
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Design a coherent system of workflows — Combine multiple automations into a functioning operation, document them for your future self, and know when you've outgrown no-code automation.
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Articulate the difference between workflows and autonomous agents — Understand what static automations can do, recognize when you need an agent instead, and know what the next step is (AgenticAI course).
Prerequisites
Next Steps
AI Workflows — Outline
Common Questions
Do I need to know how to code?
A: No. This course is designed for people comfortable with web tools and SaaS apps, but without any coding experience required. You'll understand how APIs work at a practical level—what they are and why you use them—but you won't be writing code. If you can navigate Zapier, Google Sheets, or Notion, you have the technical foundation this course expects.
What automation tools does this course cover?
A: The course uses two main platforms (your choice): - Make — Cloud-hosted, visual interface, large connector library. Fastest to results. No data privacy control. - n8n — Self-hosted, runs on your machine or a server you control. More setup, more power, complete data ownership. Naturally leads into the AgenticAI course.
All exercises work in both. You pick your path on day one and follow it throughout.
What's the difference between "AI Workflows" and "AgenticAI" (course 401)?
A: Workflows are static automation that runs the same steps every time, with AI as one optional step. Agents are autonomous—they reason about their goals, decide what to do, and adapt. A workflow emails you a summary every morning. An agent sees your morning email is overloaded, decides to triage it, and tells you what's actually urgent. Take AI Workflows first to understand the foundations; AgenticAI assumes you know how to connect tools and think about automation.
What's the difference between a "workflow" and an "agent"?
A: A workflow is a fixed sequence: trigger → action → action → action → done. You define all the steps in advance. An agent is goal-oriented: given an objective, it decides what steps to take, executes them, checks the result, and adapts. Workflows run on a schedule or trigger. Agents can loop, reason, and adjust in real-time. Workflows are predictable; agents can surprise you. This course is workflows. The next course (AgenticAI) is agents.
What's the time commitment?
A: The course is designed for 2–3 weeks at part-time pace—roughly 2–3 hours per day, 4–5 days per week. You're learning by building, not watching videos, so the time is spent actually setting up automations in your platform of choice, testing them, and troubleshooting when they break (they will). If you already know one automation platform well, you'll move faster. If this is your first time, budget the full time.
What are the prerequisites?
A: You should have completed IntroToAI (course 101) or have equivalent experience. That means: - You're comfortable using AI tools (ChatGPT, Claude, etc.) conversationally. - You understand what an LLM is and what it can and can't do. - You know how to write a basic prompt and evaluate an AI's output. - You're familiar with web-based SaaS apps (Google Sheets, Gmail, Slack, Notion in a professional context).
If you haven't taken IntroToAI but have solid practical experience with AI tools and cloud applications, you should be fine. This course doesn't assume you know anything about automation—it starts from zero on that front.
Will I build workflows I can actually use?
A: Yes. Every module ends with a workflow you build for a real problem you have. Module 2 is a daily morning briefing. Module 3 is inbox triage. Module 4 is connecting a tool you actually use. Module 5 is two more automations you identified in Module 1 as problems worth solving. You leave with a working system you'll use immediately.
What if my workflow breaks in production?
A: You'll learn to monitor for failures and design automations that tell you when something's wrong before you notice the work isn't getting done. Module 5 covers this explicitly. Early modules teach error handling patterns. You'll also learn the troubleshooting mindset: what broke, why, and how to fix it. This skill transfers to every workflow you build after the course.
Can I use this for my business or team?
A: Yes, but with caveats. Single-user workflows (your personal automations) scale easily. Team workflows require more thought about data flow, documentation, and handoff. The course focuses on building for yourself; the principles transfer to team automation, but you may need to extend what you learn here. If you're building workflows to delegate or use across a team, plan for extra documentation and testing time.
Glossary
API — Application Programming Interface A standardized way for two software applications to communicate. Your workflow calls an API to fetch data, send information, or trigger an action in another tool. Example: your workflow uses the OpenWeather API to fetch weather data.
CLI — Command Line Interface A text-based way to interact with software. Not required for this course, but mentioned when discussing self-hosted automation tools and how to configure them.
CRON — Clock Daemon (or Cron Job)
A scheduling system (used in n8n and Linux-based servers) that runs tasks at specific times. A cron expression like 0 7 * * * means "run at 7:00 AM every day." More precise than visual schedulers; used in the self-hosted n8n track.
CSV — Comma-Separated Values A simple text-based file format for storing tabular data (like a spreadsheet). Workflows often export or import CSV files when connecting tools.
FAQ — Frequently Asked Questions Common questions and answers about a topic. You're reading one now.
FYI — For Your Information A notification category meaning the information is useful but doesn't require immediate action. Used as an example in Module 3 when classifying emails.
GPIO — General Purpose Input/Output Hardware pins on self-hosted servers that can control physical devices. Mentioned when discussing automation possibilities beyond the scope of this cloud-based course.
HTTPS — HyperText Transfer Protocol Secure The secure version of HTTP used for web communication. All API calls in this course use HTTPS to protect your data in transit.
HTTP — HyperText Transfer Protocol The underlying protocol for web communication. When your workflow makes an API call, it's using HTTP (GET, POST, PUT, DELETE requests). Module 4 covers this in depth.
IFTTT — If This Then That A well-known automation platform that inspired the trigger-action paradigm. Similar to Zapier and Make, but simpler and less feature-rich. Mentioned in the context of the broader automation tool landscape.
JSON — JavaScript Object Notation A structured text format for storing and exchanging data, commonly used for API responses. Example:
{
"temperature": 72,
"condition": "Partly Cloudy"
}
Module 3 emphasizes getting AI to return JSON so downstream steps can parse the output reliably.
LLM — Large Language Model A neural network trained on vast amounts of text, capable of generating human-like responses. ChatGPT, Claude, and GPT-4 are LLMs. A core component of Module 3, where you add an LLM as a processing step in a workflow.
OAuth — Open Authorization An authentication standard that lets you grant an app permission to access your data without sharing your password. When you connect Gmail to your workflow platform, OAuth is often the authentication method.
PDF — Portable Document Format A file format for documents designed to look the same across different devices. Workflows often convert PDFs to text or extract data from them.
REST — Representational State Transfer An architectural style for designing APIs that use HTTP methods (GET, POST, PUT, DELETE) to interact with resources. When the course mentions "REST API," it means an API that follows REST principles. Module 4 covers this in practical terms.
RSS — Really Simple Syndication A format for distributing frequently updated content (news, blogs, podcasts) as a data feed. Workflows can subscribe to RSS feeds to stay notified of new content. Example in Module 2.
SaaS — Software as a Service Cloud-based applications you access through a web browser (Gmail, Slack, Notion, Google Sheets). The course assumes you're comfortable with SaaS apps.
SQL — Structured Query Language A language for querying and managing databases. Not required for this course, but mentioned as a tool for filtering and transforming data in some advanced scenarios.
SSH — Secure Shell A secure way to remotely access and control a computer or server, used when setting up self-hosted automation tools. Relevant to the n8n (self-hosted) track.
UI — User Interface The visual elements and controls a user interacts with. Make has a visual UI; n8n has a node-based UI. This course is entirely UI-based (no command-line work required).
URL — Uniform Resource Locator
The web address (like https://example.com/path). Workflows often construct URLs dynamically to call APIs or send data to web applications.
UUID — Universally Unique Identifier A long string of characters that uniquely identifies something, often used by APIs and automation platforms to reference objects (workflows, tasks, etc.).
YAML — YAML Ain't Markup Language A human-readable data format often used in configuration files. You may encounter it when exporting/importing workflows in n8n, but you don't need to write it.
ZIP — File Archive Format A compressed file container. Workflows and configurations are sometimes exported as ZIP files for backup or sharing. You'll encounter this when exporting n8n workflows.
The Fine Print
Action A step in a workflow that does something: sends an email, creates a spreadsheet row, calls an API, formats text. Actions are always triggered by a trigger or another action. Module 2 covers actions in detail. See also: Module, Step.
Anthropic The company behind Claude, an LLM used for automation workflows. Alongside OpenAI, Anthropic is one of the primary providers for AI steps in this course. You'll use their API key to authenticate AI modules in your workflows.
Authentication The process of proving you have permission to use a service. In workflows, this usually means providing an API key, password, or OAuth token. Module 4 covers authentication patterns for APIs.
Automation Map (or Workflow Map) A planning document that identifies repetitive tasks and sketches how they could be automated. The deliverable of Module 1. Forces you to think in terms of triggers, steps, and outcomes before you touch your automation platform.
Automation Platform Software that lets you build workflows visually without coding. Examples: Make, n8n, Zapier, IFTTT. This course uses Make (visual/cloud) and n8n (self-hosted). Different platforms have different connector libraries, pricing, and UI philosophies.
Bearer Token A type of API credential (a security token) you include in API requests to prove you're authorized. Similar to an API key but often used for OAuth. Module 4 covers this.
Branching (or Conditional Logic) Workflow logic that runs different steps based on a condition. "If status is 'urgent,' email me. If status is 'low priority,' skip this step." Module 2 covers filters and conditions. Essential for workflows that make decisions.
Caching Storing data temporarily so you don't have to fetch it repeatedly. Workflows sometimes use caching to reduce API calls and costs. Relevant when optimizing AI-heavy automations.
Claude Anthropic's flagship LLM, available in several versions (Claude Haiku, Claude 3.5 Sonnet, Claude Opus). Used as an example AI provider throughout the course. You can use Claude in your automation workflows by connecting to Anthropic's API.
Connector (or Integration) A pre-built connection between your automation platform and another tool (Gmail, Slack, Google Sheets, etc.). Make and n8n have large connector libraries. When a connector doesn't exist, you use webhooks or direct API calls (Module 4).
Cron Expression
A text-based way to define schedules (used primarily in n8n). 0 7 * * * means "7:00 AM every day." More precise than visual schedulers. Taught in Module 2 for the n8n (self-hosted) track.
CSV Export Saving workflow output (often from Google Sheets or database queries) as a CSV file that can be imported into another tool. Common in data-heavy workflows.
Data Mapping The most critical skill in automation. Connecting an output from one step to an input in the next step. If Step 1 fetches a customer's email and Step 2 sends an email, you map the output from Step 1 into the "To" field of Step 2. Module 2 (Lesson 4) is entirely devoted to this.
Data Pipeline A sequence of steps that transforms data from input to output. "Raw data goes in, data is filtered, enriched, formatted, and sent to Google Sheets." A workflow is a type of data pipeline.
Debugging The process of finding and fixing problems in a workflow. If a workflow fails, you debug by checking each step's output, examining logs, and identifying where the problem occurred. Module 5 covers debugging methodology.
Decision Tree A logic diagram that maps out different paths based on conditions. Used in planning workflows before building them. If you're automating something with multiple branches ("if urgent, do X; if normal, do Y"), a decision tree helps you visualize it.
Dockerfile A file that defines how to containerize an application (run it in isolation). Mentioned in the context of self-hosted n8n setup, but you don't need to understand it in detail.
Downstream Step (or Downstream Action) An action that comes after the current step and depends on its output. "The email step is downstream of the formatter step" means the email step uses the output of the formatter. Understanding data flow (upstream → downstream) is essential.
Edge Case An unusual or unexpected input to a workflow that might break it. "What if the weather API is down?" "What if an email has no subject?" Designing for edge cases prevents workflows from silently failing. Module 2 introduces error handling; Module 3 addresses AI-specific edge cases (hallucinations).
Email Module A pre-built workflow step that sends emails. Available in most automation platforms. You configure the recipient, subject, and body. Module 2 uses this for the Morning Brief.
Endpoint (or API Endpoint)
A specific URL in an API that performs a particular function. Example: https://api.openweather.com/data/2.5/weather is an endpoint that returns weather data. Workflows often call multiple endpoints from different APIs.
Environment Variable A value stored outside your workflow code that the workflow can reference. Used for storing API keys securely instead of hardcoding them. Module 4 touches on this when discussing authentication best practices.
Error Handling The process of designing a workflow to gracefully handle failures. Instead of breaking silently, a well-designed workflow logs the error, notifies you, or takes an alternate path. Module 2 (Lesson 6) covers this extensively.
Execution History (or Run History) A log of every time your workflow ran, including what it did, how long it took, and whether it succeeded or failed. Used to verify a workflow is running and to debug failures. You'll check execution history in Module 2 to confirm your Morning Brief is working.
Filter A condition that allows a workflow to skip steps or branch. "Only send email if status is 'urgent'." Filters prevent unnecessary actions. Module 2 covers filters and conditions.
Formatter (or Text Formatter) A workflow step that transforms raw data into a specific format. Example: "Take weather data and write it as 'Temperature: 72°F, Conditions: Sunny'." Module 2 uses formatters to turn raw data into readable email text.
GET Request An HTTP method to retrieve data from an API without modifying anything. Example: fetching weather data, checking the status of an order. Most read-only operations use GET. See also: POST Request.
GPT-4 (or GPT-4o, GPT-4-Turbo) OpenAI's flagship LLM models. Different versions trade off between speed, cost, and capability. GPT-4o-mini is cheap and effective for many automation tasks. Used in examples throughout the course.
Hallucination When an AI generates plausible-sounding but false or made-up information. In a workflow, a hallucination isn't immediately obvious (no human is reading the output), so it can propagate. Module 3 (Lesson 5) is entirely devoted to defending against hallucinations in pipelines.
HTTP Request (or HTTP Module) A workflow step that makes a raw API call (GET, POST, etc.). Used when the automation platform doesn't have a pre-built connector for a tool. Module 4 covers HTTP modules in detail.
IFTTT A well-known automation platform ("If This Then That"). Simpler than Make and n8n but with fewer connectors. Mentioned as an option in the broader automation tool landscape.
Inbox Triage (or Email Triage) A practical workflow that reads incoming emails, classifies them, summarizes them, and logs them to a database. The primary example in Module 3 for AI-enhanced workflows.
Incoming Webhook (or Webhook Trigger) A URL that your workflow provides to external systems. When something happens in the external system (new form submission, Slack message, GitHub push), it sends data to the webhook, triggering your workflow. Module 2 covers webhook triggers; Module 4 covers webhooks in depth.
JSON Schema A standard for defining the structure of JSON data. Some automation platforms use JSON schemas to help you understand what data structure an API returns. Not required knowledge, but helpful when working with complex APIs.
LLM API The interface for calling a large language model (Claude, GPT-4, etc.) from your workflow. You provide a prompt and data, and the API returns generated text. Module 3 is entirely about integrating LLM APIs into workflows.
Logic Gate (or Decision Node) A workflow element that decides which path to take based on a condition. If the condition is true, do X; if false, do Y. More complex than a simple filter.
Loop (or Iterating, Batch Processing) A workflow pattern that repeats a set of steps for each item in a list. "For each email in my inbox, classify it." Loops are essential for batch processing. Available in most platforms but has performance implications.
Make (formerly Integromat) A cloud-based visual automation platform. Large connector library. Easy to learn. No data privacy control (your data flows through Make's servers). The primary visual track for this course.
Module (in Automation Platform Context) A single step in a workflow (an action, trigger, or filter). Not to be confused with course modules. Example: "the Email module sends an email." See also: Step, Node.
Morning Brief The primary workflow you build in Module 2. It runs every morning at 7am, fetches weather data (optionally news), formats it into a readable summary, and emails it to you. The foundational example for learning workflow mechanics.
n8n A self-hosted or cloud-hosted automation platform. More powerful than Make but requires more setup. Full data ownership (your data stays on your server). Naturally leads into AgenticAI. The self-hosted track for this course.
Node (in n8n Context) The n8n equivalent of a "module" in Make. A single step in your workflow (trigger, action, filter). See also: Module, Step.
No-Code Automation Building workflows without writing code. This entire course is no-code. You use visual interfaces, drag-and-drop connections, and pre-built modules. See also: Low-Code.
OAuth An authentication standard that lets workflows request permission to access your data (Gmail, Slack, Google Sheets) without storing your password. When you "Connect Gmail" to your workflow, you're usually using OAuth.
OpenAI The company behind ChatGPT, GPT-4, and other LLMs. Alongside Anthropic, OpenAI is one of the primary AI providers for automation workflows. You'll use their API key to add AI steps.
Outgoing Webhook (or Webhook Action) A workflow step that sends data to an external system via a webhook. When your workflow is done, it can POST data to another platform's webhook, triggering something there. Module 4 covers this.
Pipeline A sequence of steps that process data. A workflow is a type of pipeline. "Data flows through a pipeline of formatting, filtering, and sending steps."
POST Request An HTTP method to send data to an API, creating or modifying something. Example: "Create a new Google Sheet row with this data." See also: GET Request.
Prompt (in Automation Context) The instruction you give an LLM in an AI step. Unlike conversational prompts (which are flexible), automation prompts must be precise, handle edge cases, and produce consistent output. Module 3 (Lesson 2) is entirely about writing automation prompts.
Rate Limit A restriction on how many API calls you can make in a given time period. Workflows that call AI heavily can hit rate limits. Module 3 (Lesson 6) covers rate limit management and cost control.
REST API An API that uses HTTP methods (GET, POST, PUT, DELETE) to interact with resources. Most modern APIs are REST APIs. Module 4 explains REST principles in practical terms.
Runbook (or Playbook) Documentation for how to operate and troubleshoot a workflow. Module 5 emphasizes creating runbooks so future-you (or a colleague) can understand and fix your automations.
Scheduled Trigger (or Cron Trigger) A workflow trigger based on time: "Run every morning at 7am" or "Run every Friday at 2pm." The Morning Brief uses a scheduled trigger. Module 2 covers scheduled triggers in detail.
Schema The structure of data (what fields it has, what types they are). When debugging workflows, understanding the schema of data at each step prevents mapping errors. See also: JSON Schema.
Scheduler The workflow component that manages time-based triggers. You define "7:00 AM every day" in the scheduler, and it starts the workflow at that time.
Self-Hosted (or Self-Hosted Automation) Running automation software on a server you control (your computer, a cloud server like AWS, etc.) rather than using a cloud service. n8n can be self-hosted. Offers more privacy and control but requires more setup. Recommended for this course if you're planning to continue to AgenticAI.
Slack A team communication platform with notification and bot capabilities. Many workflows integrate with Slack (posting messages, reacting to events). A common connector in automation platforms.
System Prompt The instruction given to an AI model that defines its role and behavior. Example: "You are a morning briefing writer. Read weather data and summarize the key points in 2-3 sentences." Module 3 emphasizes system prompt design for reliability.
Template A reusable pattern or starting point. Workflows can be saved as templates and reused for similar tasks. Used when building multiple similar automations.
Test Run (or Test Trigger) Manually executing a workflow to verify it works before putting it on a schedule. Essential before activating a workflow. You test with real or sample data, check the output, and fix any issues.
Trigger The event that starts a workflow. Examples: "a new email arrives," "7:00 AM," "a form is submitted," "a webhook receives data." Every workflow has exactly one trigger. Module 2 covers six major trigger types.
Upstream Step (or Upstream Action) An action that comes before the current step and produces data that the current step uses. "The weather API step is upstream of the formatter" means the formatter depends on the output of the weather API.
Validation Checking that data is in the correct format before using it. A workflow might validate that an email address is properly formatted before sending to it. Prevents bad data from breaking downstream steps.
Variable (or Field Variable)
A placeholder for data that gets filled in at runtime. In Make, you drag and drop blue pill variables. In n8n, you use expression syntax. Example: {{ weather_temperature }} is a variable that holds the actual temperature value when the workflow runs.
Webhook A way for two systems to communicate by sending data via HTTP POST. When something happens in Service A, it sends data to Service B's webhook URL, triggering an action in Service B. Essential for connecting tools without pre-built integrations. Module 4 covers webhooks extensively.
Webhook URL The unique URL your workflow provides to external systems. External systems POST data to this URL to trigger your workflow. Effectively a "mailbox" for your workflow.
Workflow Canvas (or Canvas) The visual interface where you build your workflow (in Make or n8n). You drag modules/nodes onto the canvas and connect them. Module 2 describes what you'll see on the canvas.
Workflow Execution A single run of your workflow from start to finish. Example: your Morning Brief workflow executes at 7:00 AM, fetches weather, formats it, and sends an email. One execution. You can view execution history to see what happened.
Workflow Library (or Workflow Collection) A collection of workflows you've built that solve related problems. Module 5 emphasizes organizing and maintaining a workflow library so you can reuse and adapt workflows instead of starting from scratch.
Zapier A cloud-based automation platform similar to Make. Large connector library. Easy to use. Not the primary platform for this course (Make and n8n are), but mentioned as an option in the automation landscape. The concepts transfer directly.