prompt-engineering

Secure LLM prompts against prompt injection and data leakage by applying the OWASP LLM Top 10 security checklist to audit system instructions, context assembly, and tool-use boundaries to mitigate excessive agency risks.

An eval-first workflow for prompt engineering establishes baseline metrics and regression testing before deploying changes. It uses canary deployments and structured output contracts to ensure prompt updates improve quality without introducing regressions.

Design agentic workflows with reliable instruction hierarchy by implementing prompt system architecture patterns for context assembly, multi-agent orchestration, and strict output contracts to ensure predictable model behavior.

Yes, prompt templates support structured output contracts in multi-agent systems by providing standardized patterns for instruction hierarchy and context assembly, ensuring reliable communication and predictable model behavior across agents.

The best way to audit existing system prompts for LLM security vulnerabilities is to evaluate them against the OWASP LLM Top 10 checklist, identifying risks like prompt injection, data leakage, and excessive agency to suggest necessary mitigations.

LLM prompt regression tests fail after context assembly changes when baseline metrics are not aligned with the new instruction hierarchy. Implementing an eval-first workflow with canary deployments catches these quality regressions before full release.