senior-prompt-engineer

To write a reliable system prompt, define minimal, constraint-driven instructions with clear output formats and explicit edge-case handling. Use structured prompt templates and instruction hierarchies to ensure assistants behave predictably when encountering invalid inputs.

The best way to evaluate LLM behavior is to build an evaluation suite using representative and adversarial test cases. The provided eval harness scaffold measures regressions by running example-driven tests against your prompts to validate assistant and agent workflows.

Structure agent prompts by applying task decomposition, establishing instruction hierarchies, and defining explicit tool-use patterns. This modular structure ensures agents understand safety constraints and execute external tool calls predictably across complex workflows.

Yes, you can turn adhoc prompts into versioned, testable artifacts by applying prompt engineering workflows. This process transforms unpredictable prompts into measurable artifacts using templates, safety checklists, and evaluation scaffolds for reliable assistant development.

To prevent exposing system prompts, implement safety constraints and instruction clarity checklists within your prompt design. Adding adversarial test cases to your evaluation harness validates that the assistant refuses malicious requests to reveal its underlying instructions.

Structured prompt templates require ongoing maintenance to handle ambiguous user requests and avoid regressions. While they enforce output formats and safety constraints, teams must continuously debug LLM behavior and update evaluation suites as adversarial inputs evolve.