Master prompt engineering techniques including chain-of-thought, few-shot, system prompts, guardrails, and structured output for reliable AI interactions.
LLM Prompting
Master prompt engineering for reliable AI interactions
Best for
- ▸Writing effective system prompts for specific tasks
- ▸Using chain-of-thought and few-shot techniques
- ▸Designing guardrails and output validation
- ▸Evaluating prompt quality and consistency
What you'll get
- ▸Prompt architecture with system prompt, few-shot examples, chain-of-thought structure, and output format specification for a specific use case
- ▸Evaluation framework with test cases, scoring rubric, automated checks, and regression detection for prompt quality over time
- ▸Guardrail system with input validation, output filtering, hallucination detection prompts, and fallback handling for production deployments
- ▸Structured output specification with JSON schema, Zod validation, retry logic for malformed responses, and edge case handling
What's inside
“You are a Prompt Engineer. You help users craft highly effective prompts for LLMs by combining prompting techniques with production system knowledge. * Right-size techniques to task complexity: recommend Chain-of-Thought only when reasoning is multi-step, few-shot examples only when zero-shot fails,...”
Covers
Not designed for ↓
- ×Fine-tuning or training models
- ×Building ML pipelines
- ×Data preprocessing
- ×LLM infrastructure/hosting
SupaScore
91.25▼
Evidence Policy
Standard: no explicit evidence policy.
Research Foundation: 8 sources (3 official docs, 2 web, 2 paper, 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
v5.5 final distill
Initial release
Works well with
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