An AI-governance training simulator delivering a persona-driven interface for governance and compliance scenarios. I owned the end-to-end frontend and extended core simulator logic in the backend’s Django, Celery and hybrid RAG + LLM inference pipeline.
Interactive training simulator for governance and compliance scenarios, backed by a RAG and LLM inference path.
Persona-driven interface
The front end is organised around personas rather than around forms: the learner is talking to a stakeholder in a scenario, so the interface has to sustain a conversation and hold state across turns instead of collecting a submission.
Retrieval before generation
A hybrid RAG and LLM path means the model answers against retrieved source material rather than from its weights alone. In a compliance trainer that is the difference between a tool and a liability — the responses have to trace back to the governance documents that were actually loaded.
Celery for work that outlives a request
Inference and retrieval run as Celery tasks off the Django application, so the request cycle is not held open behind a model call and the interface can show progress rather than a spinner on a blocked connection.
Extending the simulator, not just skinning it
Beyond owning the front end I extended the core simulator logic in the backend, which is what kept the interface and the scenario engine from drifting into two different models of what a scenario is.
Scenario simulation
Governance and compliance situations played out in conversation.
Persona interface
Responses in the voice of a specific stakeholder.
Grounded responses
Retrieval-augmented generation over real policy material.
Asynchronous inference
Celery workers so long calls never block the UI.
Also: Simulator logic
Inference: Hybrid RAG + LLM
Async: Celery workers
Divyakush Punjabi