how to build an AI automation (a step by step breakdown):
step 1: map out the manual task
before automating anything, document how you do it manually:
- what's the step-by-step process if a human were to do this?
- what data and information do you need at each step?
- what are the key decision points?
before automating anything, document how you do it manually:
- what's the step-by-step process if a human were to do this?
- what data and information do you need at each step?
- what are the key decision points?
step 2: decide how much variance you want in outputs
this depends on the task type:
creative tasks (writing copy) = more variance allowed
predictable tasks (categorization, data entry) = less variance needed
as this determines what guardrails you'll need later on
this depends on the task type:
creative tasks (writing copy) = more variance allowed
predictable tasks (categorization, data entry) = less variance needed
as this determines what guardrails you'll need later on
step 3: compile and clean all necessary data
gather every piece of context your automation needs:
- past examples
- company documentation
- decision criteria
- output formats
clean this data and remove irrelevant or inconsistent Info
gather every piece of context your automation needs:
- past examples
- company documentation
- decision criteria
- output formats
clean this data and remove irrelevant or inconsistent Info
step 5: identify APIs and function calls needed
what external tools does your automation need to interact with?
- CRM integrations
- email APIs
- database connections
- calendar systems
u wanna map out every integration point
what external tools does your automation need to interact with?
- CRM integrations
- email APIs
- database connections
- calendar systems
u wanna map out every integration point
also determine what should be stored as memory or logged:
- what information needs to be remembered for future tasks?
- what data helps you debug when things break?
- what context improves performance over time?
notice too guys everything we've done so far is all about system design
- what information needs to be remembered for future tasks?
- what data helps you debug when things break?
- what context improves performance over time?
notice too guys everything we've done so far is all about system design
step 6: code it step by step in Cursor
now that you've mapped everything, build it out incrementally:
implement step 1 –> test –> fix issues –> move to step 2
and don't build everything at once with cursor or whatever AI coding tool you use
as you want to catch errors early
and if you build it all at once it's just going to be a mess at the end
now that you've mapped everything, build it out incrementally:
implement step 1 –> test –> fix issues –> move to step 2
and don't build everything at once with cursor or whatever AI coding tool you use
as you want to catch errors early
and if you build it all at once it's just going to be a mess at the end
step 7: add safeguards and human intervention points
identify where things could go wrong:
- where should humans review before action?
- what validation checks prevent bad outputs?
- where do errors need human escalation?
identify where things could go wrong:
- where should humans review before action?
- what validation checks prevent bad outputs?
- where do errors need human escalation?
step 8: test on a variety of inputs
create a test dataset with:
- typical use cases
- edge cases that might break it
- scenarios outside normal parameters
- jailbreaks
create a test dataset with:
- typical use cases
- edge cases that might break it
- scenarios outside normal parameters
- jailbreaks
• • •
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