Who should use the Annotate training data workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
4-step pipeline — follow each step in order
1Setup · Step 1 of 4
Open task pageGenerate synthetic data
Prepare inputs and settings through Generate synthetic data before running annotate training data.
Why this matters
Generate synthetic data sets up the foundation for annotate training data; clean inputs here reduce downstream rework.
What you get
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
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2Input · Step 2 of 4
Open task pageAutomate data labeling
Use Automate data labeling to build supporting assets that improve annotate training data quality.
Why this matters
Automate data labeling strengthens annotate training data by feeding better supporting material into the pipeline.
What you get
Supporting assets from automate data labeling are prepared and connected to the main workflow.
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3Core · Step 3 of 4
Open task pageAnnotate training data
Execute annotate training data with Annotate training data to produce the primary decision-ready insight.
Why this matters
This is the core step where annotate training data actually happens, so it determines baseline quality for everything after it.
What you get
A first-pass decision-ready insight is generated and ready for refinement in the next steps.
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4Deliver · Step 4 of 4
Open task pageConnecting data retrieval, reasoning, and training steps
Package and ship the output through Connecting data retrieval, reasoning, and training steps so annotate training data reaches end users.
Why this matters
Connecting data retrieval, reasoning, and training steps is what turns intermediate output into a usable, publishable result for real users.
What you get
A finalized decision-ready insight is ready for publishing, handoff, or integration.