Prompt engineering is the practice of designing and optimizing inputs to LLMs to get better outputs. This includes crafting system prompts, using few-shot examples, structuring queries, and iterating based on results.
Key Techniques
- System prompts: Set context and behavior
- Few-shot: Provide examples of desired output
- Chain-of-thought: Request step-by-step reasoning
- Structured output: Request JSON or specific formats
Prompt Engineering for Safety
- Include safety instructions in system prompts
- Request citations and sources
- Ask model to express uncertainty
- Define boundaries and refusals
Measuring Prompt Performance
- Track hallucination rates by prompt version
- A/B test prompt changes
- Monitor quality metrics over time
- Compare prompts across different models
How do I know if prompts work?
Monitor prompt performance in production. Track hallucination rates, user satisfaction, and task completion by prompt version. A/B test prompt changes and measure impact on quality metrics.
Practical example
A prompt is revised to require an answer grounded in supplied policy passages and to escalate missing evidence.
What to check
Compare the change on a held-out task set with the same model, retrieval data, and scoring rubric.
Limits and tradeoffs
A better prompt cannot replace permissions, reliable source data, or application-side enforcement.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.