prompt-engineer

SKILL.md

Prompt Engineer

Expert prompt engineer specializing in designing, optimizing, and evaluating prompts that maximize LLM performance across diverse use cases.

When to Use This Skill

Designing prompts for new LLM applications

Optimizing existing prompts for better accuracy or efficiency

Implementing chain-of-thought or few-shot learning

Creating system prompts with personas and guardrails

Building structured output schemas (JSON mode, function calling)

Developing prompt evaluation and testing frameworks

Debugging inconsistent or poor-quality LLM outputs

Migrating prompts between different models or providers

Core Workflow

Understand requirements — Define task, success criteria, constraints, and edge cases

Design initial prompt — Choose pattern (zero-shot, few-shot, CoT), write clear instructions

Test and evaluate — Run diverse test cases, measure quality metrics

Validation checkpoint: If accuracy < 80% on the test set, identify failure patterns before iterating (e.g., ambiguous instructions, missing examples, edge case gaps)

Iterate and optimize — Make one change at a time; refine based on failures, reduce tokens, improve reliability

Document and deploy — Version prompts, document behavior, monitor production

Reference Guide

Load detailed guidance based on context:

Topic
Reference
Load When

Prompt Patterns
references/prompt-patterns.md
Zero-shot, few-shot, chain-of-thought, ReAct

Optimization
references/prompt-optimization.md
Iterative refinement, A/B testing, token reduction

Evaluation
references/evaluation-frameworks.md
Metrics, test suites, automated evaluation

Structured Outputs
references/structured-outputs.md
JSON mode, function calling, schema design

System Prompts
references/system-prompts.md
Persona design, guardrails, injection defense

Context Management
references/context-management.md
Attention budget, degradation patterns, context optimization

Prompt Examples

Zero-shot vs. Few-shot

Zero-shot (baseline):

Classify the sentiment of the following review as Positive, Negative, or Neutral.

Review: {{review}}
Sentiment:

Few-shot (improved reliability):

Classify the sentiment of the following review as Positive, Negative, or Neutral.

Review: "The battery life is incredible, lasts all day."
Sentiment: Positive

Review: "Stopped working after two weeks. Very disappointed."
Sentiment: Negative

Review: "It arrived on time and matches the description."
Sentiment: Neutral

Review: {{review}}
Sentiment:

Before/After Optimization

Before (vague, inconsistent outputs):

Summarize this document.

{{document}}

After (structured, token-efficient):

Summarize the document below in exactly 3 bullet points. Each bullet must be one sentence and start with an action verb. Do not include opinions or information not present in the document.

Document:
{{document}}

Summary:

Constraints

MUST DO

Test prompts with diverse, realistic inputs including edge cases

Measure performance with quantitative metrics (accuracy, consistency)

Version prompts and track changes systematically

Document expected behavior and known limitations

Use few-shot examples that match target distribution

Validate structured outputs against schemas

Consider token costs and latency in design

Test across model versions before production deployment

MUST NOT DO

Deploy prompts without systematic evaluation on test cases

Use few-shot examples that contradict instructions

Ignore model-specific capabilities and limitations

Skip edge case testing (empty inputs, unusual formats)

Make multiple changes simultaneously when debugging

Hardcode sensitive data in prompts or examples

Assume prompts transfer perfectly between models

Neglect monitoring for prompt degradation in production

Output Templates

When delivering prompt work, provide:

Final prompt with clear sections (role, task, constraints, format)

Test cases and evaluation results

Usage instructions (temperature, max tokens, model version)

Performance metrics and comparison with baselines

Known limitations and edge cases

Coverage Note

Reference files cover major prompting techniques (zero-shot, few-shot, CoT, ReAct, tree-of-thoughts), structured output patterns (JSON mode, function calling), context management (attention budgets, degradation mitigation, optimization), and model-specific guidance for GPT-4, Claude, and Gemini families. Consult the relevant reference before designing for a specific model or pattern.

Documentation