Before starting a reverse engineering project, review these considerations around information governance, data handling, AI output quality, and costs. Addressing these topics early will help your team avoid common pitfalls and work within organisational policies.
- Information Governance — classification requirements and data routing
- PII Handling — mandatory steps for removing personally identifiable information
- AI Output Quality & Hallucination Risks — what to watch for when reviewing AI-generated outputs
- Cost & Token Usage — factors that affect processing costs and how to manage them
- Model Selection — choosing the right model for different tasks
- Troubleshooting — solutions to common issues
