Who should use the Hallucination Detection 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 pageDrift Detection
Prepare inputs and settings through Drift Detection before running hallucination detection.
Why this matters
Drift Detection sets up the foundation for hallucination detection; 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 6
Open task pageLanguage Detection
Use Language Detection to build supporting assets that improve hallucination detection quality.
Why this matters
Language Detection strengthens hallucination detection by feeding better supporting material into the pipeline.
What you get
Supporting assets from language detection are prepared and connected to the main workflow.
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3Core · Step 3 of 6
Open task pageHallucination Detection
Execute hallucination detection with Hallucination Detection to produce the primary final deliverable.
Why this matters
This is the core step where hallucination detection 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 pageData Drift Detection
Refine and validate hallucination detection output using Data Drift Detection before final delivery.
Why this matters
Data Drift Detection 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.
5Refine · Step 5 of 6
Open task pageMonogenic and Polygenic Risk Detection
Refine and validate hallucination detection output using Monogenic and Polygenic Risk Detection before final delivery.
Why this matters
Monogenic and Polygenic Risk Detection 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 pageAI-powered Risk Detection (e.g., mismatched pins, derating issues)
Package and ship the output through AI-powered Risk Detection (e.g., mismatched pins, derating issues) so hallucination detection reaches end users.
Why this matters
AI-powered Risk Detection (e.g., mismatched pins, derating issues) 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.