Here’s the prompt I’ve been refining. It forces the model to generate not just code, but an architectural outline for isolation and auditability:
You are a senior software architect building a secure internal tool from a natural-language spec. Follow these rules:
1. **Input**: The user describes the business need (e.g., "a risk console for fraud alerts").
2. **Output structure**:
- A list of data sources needed (e.g., Postgres, API endpoint) with access scoped to a single role.
- A schema for a dedicated Postgres role and database—queries must run through an authenticated SQL gateway.
- A React front-end with UI controls for every hardcoded threshold in the input description.
- Backend handlers that never store secrets; use environment placeholders and an egress proxy for external connectors.
- An LLM agent review step that blocks dangerous SQL or API mutations.
3. **Distribution**: The tool must be shareable as a forked object—each instance gets its own schema and role.
4. **Constraints**: No embedded keys, network policy deny-by-default, every data access logged.
Now generate the complete project scaffold for: [insert workflow description].
Why this works: It bakes security into the generation flow rather than bolting it on later. The “LLM agent review” step is key—it forces the output to include a validation layer that catches accidental data leaks or destructive operations. I tested it on a customer example from a recent YC launch: a data scientist’s fraud-detection notebook with hardcoded thresholds. The prompt turned that into a request for a risk console with slider controls for each threshold, a dedicated DB role, and audit logs wired to the company’s SSO.
The real output was a React app with a Node backend and a docker-compose
file that sets up Postgres with row-level security. The threshold values became input
sliders in the UI, and the egress proxy was a simple Express middleware checking each query against a whitelist. No tokens were ever in the repo—they were stored in a secrets manager and swapped at runtime.
I’ve found this prompt works best for workflows that are currently manual, like report generation or lead scoring. It saves hours of wiring up auth and permissions. Have you tried similar patterns for internal tool generation? The main challenge I’m still working on is handling real-time data sources without breaking the proxy layer.
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