Put model calls behind a controlled application layer with scoped data, validated outputs, and observable costs.
- LLM integration
- AI API integration
- application security
- AI observability
Keep business rules outside the prompt
Use ordinary code for access checks, calculations, and state changes. The model can help interpret or draft, but its output should not independently grant permissions or commit sensitive changes.
AI & AutomationAI & Automation
Thoughtful decisions compound over time.
Practical product work brings technical choices back to the people and workflows they are meant to serve.
Validate and minimise data
Send only the context required for the task, validate structured responses against a schema, and define retention expectations with providers. Protect secrets and personal information in logs.
Instrument the full path
Record latency, errors, model version, and cost in a way that supports investigation without over-collecting user data. A technical review can map these controls before integration work begins.
Practical application
Place model calls behind a service that checks the signed-in user's permissions, removes unnecessary personal data, validates returned JSON, and logs request IDs and latency. Require a separate confirmation step before an AI suggestion changes an order or account.
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