Who should use the Natural Language Generation workflow?
Teams or solo builders working on ai chatbot tasks who want a repeatable process instead of one-off tool experiments.
Execution Map
3-step pipeline — follow each step in order
1Setup · Step 1 of 3
Open task pagestory generation
Prepare inputs and settings through story generation before running natural language generation. Story generation capability
Why this matters
story generation sets up the foundation for natural language generation; clean inputs here reduce downstream rework.
What you get
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
More options
2Input · Step 2 of 3
Open task pageVoice Interaction
Use Voice Interaction to build supporting assets that improve natural language generation quality. Voice Interaction capability
Why this matters
Voice Interaction strengthens natural language generation by feeding better supporting material into the pipeline.
What you get
Supporting assets from voice interaction are prepared and connected to the main workflow.
3Core · Step 3 of 3
Open task pageNatural Language Generation
Execute natural language generation with Natural Language Generation to produce the primary decision-ready insight. Natural Language Generation capability
Why this matters
This is the core step where natural language generation 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.