> ## Documentation Index
> Fetch the complete documentation index at: https://api-reference.scale.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Rapid Quality - Writing instructions

> Writing Instructions: Best Practices Below are some best practices to consider as you write instructions: 1. Dive into your data: Before writing instructions, it’s important to review a representative sample of your data

# Writing Instructions: Best Practices

Below are some best practices to consider as you write instructions:

1. **Dive into your data**: Before writing instructions, it’s important to review a representative sample of your data to understand what global and edge case rules need to be written. As you’re reviewing the data, put yourself in the labeler's shoes; document areas you’d need guidance on, as well as any tricky cases that need to be addressed in the instructions.
2. **Writing instructions**:

* Structure: Following a clear structure in your instructions will help labelers better understand the rules, leading to higher quality annotations. The below structure is a good starting point:
* Summary of Task: Provide a few pithy bullets describing the basics of your task.
* Workflow: Provide the steps labelers must take to annotate your task from start to finish.
* Annotation Rules: Start with the global rules (i.e. rules that apply to all tasks, then zoom in to more specific rules). Areas you’ll likely want to touch on:

  * Minimum pixel size
  * What to label and what NOT to label
  * How to manage occlusion, truncation, and low visibility cases
  * Geometry sizing if relevant
  * How to manage cases you’re not sure about (i.e. “err on the side of selecting XXX”)
  * Etc.
* Label and Attribute Definitions and Examples
* Common Errors
* Edge Cases

1. **Communication**:

* Communicate succinctly and clearly (ie pithy bullets, use spaces to decrease cognitive load for the labeler)
* Include at least 1 image for each point you’re looking to make. Make sure each image has a description of WHY it is correctly or incorrectly annotated.

1. **Pressure test your instructions**: After writing the instructions, review your data again and make sure your instructions cover the majority (90+% of cases you see). Continue to iterate on instructions until you have hit the 90+% number.
2. **Final tips:**

* It can be helpful to include a short video walking through the instructions
* If there are certain tools / features that you expect labelers to use when annotating your data, we’d suggest mentioning them in the instructions and providing a description of how to use the tool (e.g. interpolation)