📄️ Web Chat Interfaces
The simplest way to deploy a security prompt is to paste it directly into the system prompt or custom instructions of a frontier model's web interface. This gives you immediate security guidance with zero infrastructure.
📄️ AI Coding Agents
AI coding agents operate autonomously — reading files, running commands, and making multi-file changes. Because they take more independent action than chat-based assistants, embedding security prompts into their configuration is especially important.
📄️ IDE Integrations
Most modern IDEs ship with AI coding assistants that support custom instructions or system prompts. Configuring these with a security prompt ensures that inline completions, chat suggestions, and refactoring proposals all follow secure coding practices.
📄️ API and Programmatic Usage
For automated workflows, CI/CD pipelines, and custom applications, you can deploy security prompts programmatically through LLM provider APIs. This gives you full control over prompt versioning, model selection, and runtime behavior.
📄️ Team and Enterprise Deployment
Individual developers can configure prompts in their personal tools, but teams and enterprises need consistent, auditable, and maintainable prompt deployment. This page covers strategies for scaling prompt deployment across organizations.
📄️ Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) deploys a Manicode security prompt alongside context retrieved from your own sources — internal coding standards, framework documentation, prior security findings, or threat models. At query time, the most relevant context is fetched and injected into the prompt before it reaches the model. This keeps the model grounded in your organization's specifics without baking that content into a single, ever-growing system prompt.