Learning Feature
Introduction
The Learning feature helps your assignments improve over time. When an assignment completes a task particularly well, you can capture that success as a "learning" that guides future runs.
What is Learning?
Learning allows you to tell an assignment: "This is how you should handle this type of task." By marking a successful run as a learning, you help the assignment understand what good looks like.
Think of it as positive reinforcement—showing the assignment an example of excellent work so it can replicate that success.
Why Use Learning?
Improve Consistency
When an assignment handles a task perfectly, capture that approach so future runs follow the same pattern.
Handle Edge Cases
If an assignment successfully navigates a tricky situation, save that learning so it knows how to handle similar cases.
Refine Over Time
As you work with an assignment, you can continuously improve it by adding learnings from its best performances.
Reduce Instruction Complexity
Instead of writing detailed instructions for every scenario, let the assignment learn from examples of good work.
How to Create a Learning
From Past Jobs
Go to your assignment's job history
Find a run that completed the task excellently
Click the Create Learning button at the top of the run
The assignment will now reference this run for guidance
From Current Runs
While viewing a run in progress or just completed:
If the run is going well, look for the Create Learning option
Click to save this run as a learning
Future runs will benefit from this example
How Learning Works
When you create a learning:
The assignment analyzes what made that run successful
It extracts patterns and approaches used
Future runs reference this learning for guidance
The assignment applies similar approaches to new tasks
The learning becomes part of how the assignment approaches its work—like institutional knowledge that improves performance.
Best Practices
Choose Representative Examples
Select runs that:
Completed the task correctly
Handled the typical case well
Demonstrate the approach you want
Produced the output quality you expect
Avoid Edge Cases Initially
Don't make unusual situations the learning:
Start with standard, successful runs
Edge cases can create unexpected patterns
Build a foundation with typical examples
One Learning at a Time
Each assignment carries only one learning:
Creating a new learning replaces the old one
Choose your best example
Update when you find a better example
Review Results
After creating a learning:
Monitor subsequent runs
Verify the learning improves performance
Adjust if results aren't as expected
Real-World Examples
Example 1: Email Responses
Your assignment drafts customer responses. One response was particularly well-written—professional tone, addressed all concerns, and followed brand guidelines perfectly.
Create a learning from this run. Now future responses will follow this high-quality example.
Example 2: Data Processing
Your assignment extracts data from invoices. One run correctly handled a complex invoice with multiple line items and applied the right categorization.
Create a learning from this run. The assignment now knows how to handle similar complex invoices.
Example 3: Report Generation
Your assignment creates weekly reports. One report had exactly the right structure, formatting, and level of detail.
Create a learning from this run. Future reports will follow this successful template.
Limitations
Single Learning
Assignments carry only one learning at a time:
New learnings overwrite previous ones
Choose your best example carefully
You can always update the learning later
Not Visible in Instructions
The learning influences behavior but:
Isn't shown directly in the instructions
Works behind the scenes
Complements your written instructions
Task-Specific
Learnings work best when:
The task is similar to the learned example
Inputs are comparable
Expected outputs are consistent
Things to Know
Learning helps assignments improve through positive examples
Only one learning can be active per assignment
Creating a new learning replaces the previous one
Learnings work alongside your written instructions
Choose runs that represent your best outcomes
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