Who should use the Language Model Training workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
6-step pipeline — follow each step in order
1Setup · Step 1 of 6
Open task pageTrain AI models
Prepare inputs and settings through Train AI models before running language model training.
Why this matters
Train AI models sets up the foundation for language model training; clean inputs here reduce downstream rework.
What you get
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
2Input · Step 2 of 6
Open task pageTrain machine learning models
Use Train machine learning models to build supporting assets that improve language model training quality.
Why this matters
Train machine learning models strengthens language model training by feeding better supporting material into the pipeline.
What you get
Supporting assets from train machine learning models are prepared and connected to the main workflow.
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3Core · Step 3 of 6
Open task pageLanguage Model Training
Execute language model training with Language Model Training to produce the primary final deliverable.
Why this matters
This is the core step where language model training actually happens, so it determines baseline quality for everything after it.
What you get
A first-pass final deliverable is generated and ready for refinement in the next steps.
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4Refine · Step 4 of 6
Open task pageTrain deep learning models
Refine and validate language model training output using Train deep learning models before final delivery.
Why this matters
Train deep learning models adds quality control so issues are caught before the workflow is finalized.
What you get
The final deliverable is improved, validated, and prepared for final delivery.
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5Refine · Step 5 of 6
Open task pageExplore different model architectures and training techniques
Refine and validate language model training output using Explore different model architectures and training techniques before final delivery.
Why this matters
Explore different model architectures and training techniques adds quality control so issues are caught before the workflow is finalized.
What you get
The final deliverable is improved, validated, and prepared for final delivery.
6Deliver · Step 6 of 6
Open task pageConnect to Large Language Models
Package and ship the output through Connect to Large Language Models so language model training reaches end users.
Why this matters
Connect to Large Language Models is what turns intermediate output into a usable, publishable result for real users.
What you get
A finalized final deliverable is ready for publishing, handoff, or integration.