Four steps from input to validated output.
The Forge pipeline model transforms unreliable AI generation into controlled, auditable production systems.
Define
Specify what you need using structured input specifications, not open-ended prompts. Forge uses these specifications to route tasks, validate outputs, and enable reproducibility. Your input becomes the source of truth.
Example: "Generate a 12-module training course on workplace safety for the mining sector, SCORM-compliant, assessed to AQF Level 3, with embedded quizzes and scenario-based assessments."
Define your quality rules here: content standards, compliance requirements, audience parameters, and output formats. Forge uses these rules during validation.
Run
Forge routes tasks to the right models, coordinates multi-stage generation, and manages dependencies between pipeline steps. Each step produces traceable artifacts that carry metadata about the models used, parameters, and execution time.
The pipeline is not a black box. Every step is logged. Every decision is recorded. You can trace any artifact back to the input specification, the models that generated it, and the parameters used.
If a step fails validation, Forge doesn't just report the error. It triggers repair flows: regenerating the failed component, adjusting parameters, or routing to a different model. No manual rework required.
Validate
Every output passes through validation gates before packaging. Forge runs three types of checks:
- Structural checks: Format compliance, schema validation, required fields present.
- Semantic checks: Content accuracy, coherence, logical flow, no contradictions.
- Quality checks: Readability, instructional design principles, compliance with your style guides.
Validation reports are generated alongside every artifact. You get confidence scores, pass/fail status, and detailed findings. Validation is not hidden—it's part of your audit trail.
Export
Validated artifacts are packaged into delivery-ready formats. Forge doesn't export raw AI output. It exports complete, validated, traceable systems.
For the Course Engine, this means: SCORM packages, QTI assessments, lesson documents, facilitator guides, marking rubrics, and model answers. Every export includes a validation certificate proving it passed all checks.
Every artifact carries a run trace: which input specification generated it, which models were used, which validation checks passed, when it was generated. Full reproducibility.
What makes it different
Reproducible
Run the same pipeline twice with the same input, get validated outputs to the same standard. Not identical—validated to the same quality bar.
Traceable
Every artifact links back to the pipeline run, input specification, and validation report that produced it. Full audit trail, always.
Repairable
When validation fails, Forge knows which step broke and how to fix it. Automatic repair flows. No manual debugging.
See how Forge handles real-world training content generation.
See it in action