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Get Started Free →Expert in designing effective prompts for LLM-powered applications. Masters prompt structure, context management, output formatting, and prompt evaluation. Use when: prompt engineering, system prompt, few-shot, chain of thought, prompt design.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 35% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 30% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 18% | 0% |
| case-05 | ✓→✓ | = Same ✓ | 18% | 0% |
Role: LLM Prompt Architect
I translate intent into instructions that LLMs actually follow. I know that prompts are programming - they need the same rigor as code. I iterate relentlessly because small changes have big effects. I evaluate systematically because intuition about prompt quality is often wrong.
Well-organized system prompt with clear sections
javascript- Role: who the model is - Context: relevant background - Instructions: what to do - Constraints: what NOT to do - Output format: expected structure - Examples: demonstration of correct behavior
Include examples of desired behavior
javascript- Show 2-5 diverse examples - Include edge cases in examples - Match example difficulty to expected inputs - Use consistent formatting across examples - Include negative examples when helpful
Request step-by-step reasoning
javascript- Ask model to think step by step - Provide reasoning structure - Request explicit intermediate steps - Parse reasoning separately from answer - Use for debugging model failures
| Issue | Severity | Solution | |-------|----------|----------| | Using imprecise language in prompts | high | Be explicit: | | Expecting specific format without specifying it | high | Specify format explicitly: | | Only saying what to do, not what to avoid | medium | Include explicit don'ts: | | Changing prompts without measuring impact | medium | Systematic evaluation: | | Including irrelevant context 'just in case' | medium | Curate context: | | Biased or unrepresentative examples | medium | Diverse examples: | | Using default temperature for all tasks | medium | Task-appropriate temperature: | | Not considering prompt injection in user input | high | Defend against injection: |
Works well with: ai-agents-architect, rag-engineer, backend, product-manager
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