Forage Academy Data Labeling

Why complete this Job Simulation

A risk-free way to experience work on the job with us at Forage Academy. Practice your skills with example tasks and build your confidence to ace your applications.

Self-paced

3-4 hours

No grades

Optional assessment

Introductory

Welcome to the Forage Data Labeling Simulation!

We're excited to have you join us for an immersive, hands-on journey into the world of modern AI data operations. Whether you're curious about how labeled data powers machine learning or you're eager to test your eye for detail, this program is your launchpad.

Across two engaging tasks, you'll step into the shoes of an entry-level data labeler and then a quality reviewer. You'll apply simple guidelines to short customer-support messages, practice privacy-aware labeling by flagging PII, and learn how small decisions affect model behavior. You'll also review pre-labeled items, make Keep/Fix/Flag calls with brief justifications, and propose targeted improvements to the guidelines, just like real teams do.

Get ready to learn, experiment, and show off your judgment, as you discover what it takes to produce clear, consistent, and ethical labels that make AI work.

How it works

Complete tasks guided by pre-recorded videos and example answers from our team at Forage Academy. No live sessions, all self-paced.
Earn a certificate and add it to your resume and LinkedIn as an extracurricular activity.
Stand out in applications. Confidently answer interview questions and explain why you’re a good fit for our team.

Skills you will learn and practice:

Analytical Thinking

Attention to Detail

Data Privacy

Documentation

Feedback Delivery

Tasks

Intro & Scenario

Background context and your project team

Your role

Entry-level Data Labeler on a help center AI project
Apply a small schema: Intent, Sentiment, and PII Flag
Use brief rationales for designated edge cases
Note ambiguities and propose one guideline clarification

Your goal

Produce clear, consistent labels for a small batch of messages
Practice privacy-aware judgment by identifying and flagging PII
Build confidence with guidelines so similar items get similar labels
Prepare for basic reviewer decisions you’ll practice later in the simulation

Intro & Scenario

Background context and your project team

Your role

Entry-level Data Labeler on a help center AI project
Apply a small schema: Intent, Sentiment, and PII Flag
Use brief rationales for designated edge cases
Note ambiguities and propose one guideline clarification

Your goal

Produce clear, consistent labels for a small batch of messages
Practice privacy-aware judgment by identifying and flagging PII
Build confidence with guidelines so similar items get similar labels
Prepare for basic reviewer decisions you’ll practice later in the simulation

Reviews

4.6

The simulation provided a practical, realistic experience with data labeling and quality review. The guidelines were clear, edge cases posed a challenge, and the "Keep/Fix/Flag" methodology is directly applicable to real-world AI projects. I particularly appreciated the focus on PII awareness and consistency.

Student

I liked that the simulation provided realistic data-labeling scenarios and required me to make practical decisions about intent, sentiment, and PII. The review task was especially useful because it helped me understand quality control and the importance of consistency when working with labeled data.

Student

You've now reviewed five real support messages, spotted a label error, and practiced making judgment calls. Along the way, you've probably noticed places where the definitions or examples in the One Page Guidelines could be clearer. Clear, simple instructions help everyone label more consistently, and your insight could make a big difference.

Student, Visvesvaraya Technological University