prompt-engineer

Design system prompts by defining specific personas, setting safety boundaries, and implementing injection defenses. This creates predictable model behavior and protects LLM applications from malicious inputs.

Refactor prompts by streamlining instructions to improve accuracy, stability, and token efficiency. This reduces API costs and ensures more consistent structured outputs from LLMs.

Build JSON schemas and function-calling structures by creating structured output guidelines for reliable parsing. This ensures LLMs satisfy required formats and return valid JSON consistently.

Create an evaluation framework by building rubrics, test suites, and metrics to measure prompt quality and model performance. This enables faster experimentation and validates prompt improvements.

Yes, implement chain-of-thought or few-shot learning techniques within prompts to maximize performance. These methods guide LLMs through reasoning steps or examples for better accuracy.

Inconsistent structured outputs occur when prompts lack clear JSON schemas or evaluation frameworks. Fix this by refactoring prompts for stability and applying structured output guidelines for reliable parsing.