daemon-blockint-tech Prompt Engineer
Overview
Design, test, and optimize prompts for LLM interactions. This skill covers prompt patterns (few-shot, chain-of-thought, ReAct), system prompt design, output formatting, prompt evaluation, and prompt optimization techniques.
Features
- Prompt patterns: few-shot, zero-shot, chain-of-thought, ReAct, self-consistency
- System prompt design: role definition, constraints, output format specification
- Output formatting: JSON, XML, markdown, structured templates
- Prompt evaluation: quality metrics, consistency testing, edge case analysis
- Prompt optimization: token reduction, clarity improvement, robustness testing
Usage
- Identify the user's prompt need (pattern selection, system prompt, output format, or optimization)
- Follow the corresponding workflow below
- Produce structured outputs: prompt templates, system prompts, output schemas, or evaluation reports
Examples
User: "Write a prompt for summarization" Agent: Runs Prompt Design workflow, selects zero-shot pattern, defines role and constraints, produces prompt with output format
User: "Optimize this prompt" Agent: Runs Prompt Optimization workflow, identifies ambiguity, reduces token count, adds clarity, tests edge cases
User: "Evaluate prompt quality" Agent: Runs Prompt Evaluation workflow, tests against quality metrics, identifies failure modes, produces improvement recommendations
When to Use
- Designing, versioning, and evaluating prompts for LLM-powered features
- Building agent workflows (ReAct, tool use, multi-agent coordination)
- Optimizing accuracy, format compliance, latency, and token cost
- Deploying guardrails, observability, and abuse defenses for GenAI in production
When NOT to Use
- Classical ML model training, feature engineering, or statistical A/B tests → use
data-scientist - General technical writing, API reference, or runbooks → use
tech-writer-researcher - Cloud infrastructure, CI/CD, or Kubernetes operations → use
infrastructure-engineer - Revenue recognition or finance close procedures → use
senior-revenue-accountant - Multi-feature token reduction roadmap → use
ai-token-improvement-plan-engineer - Rigorous token-efficiency experiments and ablations → use
research-engineer-scientist-tokens
Core Workflows
1. Prompt Design Workflow
Step-by-step process:
Define the task clearly
- What input does the user provide?
- What output format is required?
- What constraints must be enforced?
Choose the pattern
Pattern When Structure Zero-shot Simple, well-defined tasks Instructions + input Few-shot Pattern recognition, formatting Examples + task Chain-of-thought Reasoning, math, logic "Let's think step by step" Role-based Domain expertise needed "You are a senior X..." Structured API/programmatic consumption JSON schema, XML template Draft and iterate
- Start simple, add complexity only where needed
- Use clear separators (###, XML tags, markdown)
- Specify output format explicitly
- Include constraints and what to avoid
Test with edge cases
- Empty input, malformed input, adversarial input
- Boundary conditions
- Multiple languages or formats
2. Prompt Optimization & Testing
Evaluation dimensions:
- Accuracy: Does it produce correct results? (human or model judge)
- Consistency: Same input → same output? (temperature, seed control)
- Format compliance: Does output match the schema? (JSON validator)
- Latency: Time to first token, total generation time
- Cost: Tokens consumed (input + output)
Testing workflow:
- Build a benchmark dataset (50-200 diverse examples)
- Establish baseline with current prompt
- Modify one variable at a time (prompt, model, temperature)
- Run A/B comparison on benchmark
- Measure and document improvement
3. Agent Orchestration
Agent patterns:
| Pattern | When | Components |
|---|---|---|
| ReAct | Tool-using agent | Reasoning + Action + Observation loop |
| Plan-and-Solve | Multi-step tasks | Planner → Executor → Checker |
| Reflexion | Self-improvement | Execute → Evaluate → Revise |
| Multi-agent | Complex workflows | Specialist agents + coordinator |
Tool use checklist:
- Tool schemas are clearly defined (name, description, parameters)
- Agent can handle tool failure gracefully
- Tool results are summarized, not passed raw to user
- Rate limits and costs are monitored
4. Production Patterns
Security checklist:
- Input validated and sanitized
- Prompt injection defenses in place (delimiters, output filtering)
- No sensitive data in prompts (PII, secrets)
- Output filtered for harmful content
- Rate limiting and abuse detection
Observability:
- Log all prompts and responses (with PII redaction)
- Track token usage and cost per user/request
- Monitor for drift in output quality
- Alert on error rates and latency spikes