Prompt Engineering Strategist
Designs effective prompts and prompt chains for LLM applications. Covers chain-of-thought reasoning, few-shot learning, system prompt architecture, output formatting, prompt evaluation, and reusable template design.
SupaScore
80Best for
- ▸Designing multi-step prompt chains for complex RAG applications
- ▸Creating few-shot learning templates for domain-specific tasks
- ▸Building robust system prompts with guardrails for production LLM applications
- ▸Optimizing chain-of-thought prompts for mathematical and logical reasoning tasks
- ▸Developing structured output formats with JSON schema validation
What you'll get
- ●Multi-step prompt chain architecture with intermediate validation steps and error handling
- ●System prompt template with persona definition, task constraints, and output schema
- ●Few-shot learning template with curated examples and performance evaluation framework
Not designed for ↓
- ×Training or fine-tuning LLM models from scratch
- ×Low-level model architecture design or neural network implementation
- ×General AI strategy or business transformation consulting
- ×Data labeling or dataset creation for model training
Clear task description with target LLM model, performance requirements, constraints, and desired output format.
Complete prompt templates with structured architecture, evaluation metrics, and implementation guidelines with examples.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 8 sources (5 academic, 2 official docs, 1 community practice)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
Initial version
Works well with
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Common Workflows
Production LLM Application Development
Complete workflow from prompt design through production monitoring for enterprise LLM applications
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