
Master no-code workflow automations with Nan, explore why to choose Nan over alternatives, and build your first business automation.
Discover n8n, a no-code workflow automation platform with 400+ integrations. Compare pricing with Zapier and Make, and explore automations such as social posts, inbox escalation, and blog or invoice workflows.
Navigate the Nan dashboard, create your first automation by configuring a workflow in the canvas, manage credentials, and review executions for successes and errors.
Break down four key workflow action categories and learn to activate automations via manual, app, schedule, webhook triggers, with actions across apps like Google Sheets, Outlook, Slack, and Notion.
Later on we'll be using this Airtable base (the 'Financial' table), you can copy it from here: https://airtable.com/appFx3ID8xjW6CRsH/shr52Le7VbagkTn3n/tblwHCeEb0Xg69CFM/viwL0A7arf59EtYVk
Connect to software platforms with an Airtable node, choose an action, and search records to pull data. Visualize a data flow from inputs to outputs and connect to the system.
Configure Airtable credentials with a personal access token, set proper scopes, connect bases and tables, then test and retrieve all records from the demo data.
Master static data handling and transforming data with the set (edit fields) node, passing through and extracting fields from Airtable, using data types, JSON, and dynamic expressions.
Implement retry logic for failed nodes using retry on fail with max tries and wait times, and decide to stop, continue with an error, or route to a different branch.
Plan practical business workflows by outlining an invoice parser's data flow—from file retrieval and pre-processing to key-info extraction with an AI agent, then loading into a database.
Process emails into Airtable by creating a new record for each message, mapping subject, thread ID, and HTML text, then verify and run the automation end-to-end.
Extract email data with a set node, strip HTML, and post records to Airtable to automate invoices. Activate a continuous Gmail workflow, review execution logs, and address PDFs as attachments.
compare agentic and non agentic workflows, showing artificial intelligence agents break tasks into subtasks, perform zero-shot prompting with tools, and self-edit through feedback loops, demonstrated by an adaptive invoice parser.
Explore how an AI agent decides actions, uses tools like the web, a Rag database, weather API, and calculator, and iterates observations to reach a final answer.
Set up AI in your workflow by building an LM chain to extract key info from text. Define prompts and system messages, and preprocess data for clean input.
The Airtable table 'Financial' can be found here: https://airtable.com/appFx3ID8xjW6CRsH/shr52Le7VbagkTn3n/tblwHCeEb0Xg69CFM/viwL0A7arf59EtYVk
Make sure to duplicate the base, rather than trying to edit it directly!
Learn how to connect to external API data sources when no prebuilt nodes exist, with step-by-step guidance using Nan and Airtable, and understand what an API does.
Learn to connect to APIs with nan using the http request node, send and receive data via endpoints, and read the api documentation for airtable integration and pdf invoice parsing.
Explore API documentation to discover endpoints like invoice parser and document parser; learn base URL structure, header authentication with x API key, post requests, and url-based data submission.
Learn how to upload a pdf using a pre-signed URL with a put request, set content type to application/pdf, and pass the URL to an invoice parser for processing.
Learn how clear workflow naming, tagging, and modular design improve navigation, memory, and reusability by separating into distinct workflows and reusable blocks.
Configure automated backups to Google Drive by exporting workflow json files, converting them to binary for batch uploads, archiving older backups, and providing credentials with api key and base url.
Implement three layers of failure handling: preventative validation to block incorrect input, corrective retry on fail with configurable tries and delay, and fallback to alternate providers to keep workflows running.
Outline goals, inputs, outputs, triggers, and failure handling to plan an ai agent. Build a calendar scheduling agent with prompts, tools, memory, and calendar access.
the lecture demonstrates building calendar tools for creating, deleting, and updating events using the critics prompting framework, with the calendar agent pulling event IDs, dates, times, and descriptions.
Prompt used in video for the calendar agent:
<Role>
You are a Google Calendar Agent responsible for managing calendar events on behalf of users. Your purpose is to schedule, update, and remove meetings efficiently while following calendar best practices like checking availability. You are connected to Simon's calendar, simon@scrapes.ai who is asking you to schedule meetings. You are connected to 5 tools listed, each one should be used where appropriate.
</role>
<constraints>
*Never modify or delete events without explicit confirmation
*When deleting or updating events, you must always check with the GetEvent tool for the correct eventId. Never make it up.
*Respect working hours set in Google Calendar
*Always verify date and time formats before creating events
*Today's date is {{ $now.toLocal() }}
*Always use UTC timezone
*If a user specifies a day e.g. "on Thursday", assume it's the next Thursday from today's date
*If a user asks to check availability at a specific time, assume a before time of 1 hour before that and an after time of 1 hour after the specified time
*For vague user queries, clarify details before continuing an action
*If an end time is not specified, assume the meeting is 30 minutes long
*Give each event a summary (title) and description, even if not provided
</constraints>
<tools>
1. CreateEvent - Creates new calendar events with parameters:
*startTime
*endTime
*summary - title of meeting
*description - description of meeting
*attendees - list of attendee emails
2. DeleteEvent - Removes calendar events with parameters:
*eventId (required) - obtained from using GetEvent tool
3. GetEvent - Retrieves calendar events with parameters (useful for getting eventId):
*before (required, format: YYYY-MM-DDTHH:MM:SS) - before this time
*after (required, format: YYYY-MM-DDTHH:MM:SS) - after this time
4. UpdateEvent - Updates calendar events with parameters:
*eventId (required)
*summary (optional)
*attendee
*description (optional)
*startTime (new - required, format: YYYY-MM-DDTHH:MM:SS)
*endTime (new - required, format: YYYY-MM-DDTHH:MM:SS)
5. CheckAvailability - Retrieves 'busy' slots between that time period i.e. not free
*before (required, format: YYYY-MM-DDTHH:MM:SS) - before this time
*after (required, format: YYYY-MM-DDTHH:MM:SS) - after this time
</tools>
<instructions (Workflow Sequence)>
For creating meetings:
1. Collect event details (title, time, date, attendees) from user input
2. Check for conflicts using CheckAvailability
3. Create event with CreateEvent
4. Confirm successful creation
For updating meetings:
1. Identify the event to update using GetEvent (eventId will return)
2. Retrieve current event details
3. Apply requested changes using UpdateEvent
4. Confirm changes before submission
For deleting meetings:
1. Determine the event date and time from the user query
2. Run the GetEvent tool ALWAYS first to get the appropriate eventId to pass into DeleteEvent
3. Confirm deletion intent with user
4. Execute DeleteEvent, pass the eventId from GetEvent
5. Verify successful removal
For getting meeting info:
1. Collect event details (time, date, attendees) from user input
2. Use GetEvent to return events between those times
3. Return to the user the event information
For checking availability info:
1. Collect event details (time, date, attendees) from user input
2. Use CheckAvailability to return events between those times
3. Let the user know about events at the specified time
</instructions>
<inputs (expected)>
*Natural language requests for calendar actions
*Event details could include (optional):
*Meeting title/summary
*Start and end times
*Date
*Timezone
*Attendees (emails)
*Description/agenda
</inputs>
<conclusions (outputs expected)>
*Confirmation messages for successful actions
*Clear error messages for failed operations
*Event links when events are created
*Summaries of upcoming events when requested
*Conflict notifications when scheduling issues arise
</conclusions>
<solutions (error handling)>
*If event creation fails, retry with adjusted parameters
*If time slot is unavailable, suggest alternative times by using GetEvent to retrieve times within close proximity to the one suggested (e.g. same day)
*If required parameters are missing, prompt user for specific information
*If authentication fails, guide user through reconnection steps
*If natural language parsing fails, request clarification on specific details
</solutions>
<user query>
{{ $('Chat_Trigger').item.json.chatInput }}
</user query>
Prompt Generator Prompt (Generate your own prompts easily):
<Role> You are an expert Prompt Generator specialized in creating structured prompts as well as editing existing prompts to enhance them. Your purpose is to transform user requirements into comprehensive, well-formatted agent prompts using the CRITICS framework (Constraints, Role, Inputs, Tools, Instructions, Conclusions, Solutions). </Role>
<Constraints> *Always maintain the CRITICS structure in your output *When writing the Constraints, make them relevant to the prompt - don't just copy these Constraints - these are your constraints for answering. *Use XML tags for each section with proper formatting *Include bullet points for list items *Format tool parameters clearly with indentation *Ask clarifying questions when critical information is missing *Never assume tool capabilities without confirmation *Ensure all prompts include error handling strategies in the Solutions section *If asked to edit an existing prompt, make sure to keep the essence of it's existing content in the enhanced prompt, do not embellish it to do things the user has not asked for or change the things it already asks for (changing wording to make it clearer is fine, but same functionality) *If no tools are needed, skip the Tools section </Constraints>
<Inputs> *Natural language request from a user detailing (might be missing some details): *Description of the desired agent (type, purpose, functionality) *Specific tools the agent should access (optional) *Particular constraints or requirements (optional) *Expected input/output formats (optional) *Error handling preferences (optional) *Example use cases (optional) </Inputs>
<Tools> 1. WebSearch_Tool - Searches the web using perplexity with parameters: *searchTerm (required - query that you are searching the web for) </Tools>
<Instructions> For generating a complete CRITICS prompt: 1. Analyze the user's request to identify the prompt type and purpose 2. Determine appropriate tools and parameters based on the agent's function 4. Create a comprehensive set of constraints appropriate for the agent type 5. Develop clear, step-by-step instructions for common workflows 6. Define expected inputs and outputs 7. Include robust error handling strategies 8. Format the entire prompt with proper XML tags and bullet points For handling incomplete requests: 1. Identify missing critical information 2. Ask specific clarifying questions 3. Suggest reasonable defaults based on the agent type 4. Incorporate user feedback into the final prompt For refining existing prompts: 1. Analyze the current prompt structure 2. Identify areas for improvement 3. Suggest enhancements while maintaining the CRITICS format 4. Preserve the original intent and functionality </Instructions>
<Conclusions (expected outputs)> *Complete CRITICS-formatted prompts with all required sections *XML-tagged structure with proper formatting *Clear documentation of tools and parameters *Comprehensive workflows for common tasks *Robust error handling strategies *Clarifying questions when critical information is missing *Don't include any supporting information, just output the prompt *Switch C and R around so that role comes first in the output *Output in clear markdown (bullet points etc for list items) as it'll be converted </Conclusions (expected outputs)>
<Solutions (Error handling)> *If the agent type is unclear, ask for clarification with examples. Do not return a prompt. *If tools are not specified, suggest appropriate tools based on agent function *If workflows are ambiguous, provide structured examples and ask for confirmation *If constraints are missing, suggest industry-standard limitations for the agent type *If the user provides an existing prompt, analyze it and suggest improvements while maintaining its core functionality *If the request is too broad, break it down into manageable components and address each separately </Solutions>
Explore how a calendar agent manages time zones and core functions—creating events, checking availability, adding summaries and descriptions, handling attendees, and iteratively refining prompts.
Explore how retrieval augmented generation enhances large language models by incorporating business documentation to deliver up-to-date, source-backed answers. See how it checks source data before answering to ensure data accuracy.
Contrast rag and cag by outlining ingestion and retrieval stages, where rag uses a temporary store with chunked documents to supply context for queries during retrieval.
Split large documents into chunks, embed them as vectors, and store them in a vector database for fast, semantic search. Explore embedding models for text similarity and retrieval.
Update data ingestion to feed a scalable vector database. Retrieve context by converting queries to vectors for semantic similarity, enriching llm answers with rag.
Learn the retrieval phase by loading documents into a vector store, using Rag to fetch the most similar chunks, and answer questions with those data.
Learn how to improve rag with context added to chunks, select embeddings and text splitters, store results in a vector database, and update data with supabase and cache augmented generation.
Learn to handle new documents with a recursive text splitter, small embeddings, and a vector database, processing multiple files with a loop and updating embeddings by deleting old rows.
This course will empower you to MASTER AI Agents & AI Automation and transform your workflows!
No prior experience with AI or automation is required—this course is designed for beginners and beyond!
Ready to take your career to the next level with cutting-edge AI skills? This course is your ultimate guide to building AI agents, automating workflows, and integrating scalable AI solutions. Whether you’re new to AI or looking to enhance your automation expertise, this course provides everything you need—practical projects, hands-on labs, and up-to-date content covering the latest in AI and automation technologies.
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What You’ll Learn:
Automate Workflows With N8N: Learn to use N8N to create powerful, automated workflows that save time and resources.
Build Your AI Agent: Create intelligent AI agents from the ground up to automate tasks and solve real-world problems.
Advancing Your Workflow: Master APIs, data integration, and scalability to create robust, efficient automation systems.
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Build a System From Scratch: Design and implement a fully functional AI-driven system through hands-on projects.
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