mileycy516-stack Prompt Engineering Mastery
Techniques to extract maximum intelligence, reliability, and creativity from Large Language Models (LLMs).
When to Use This Skill
- Designing System Prompts for Agents
- Fixing LLM hallucinations or logic errors
- Creating "Persona" based bots
- Implementing complex reasoning tasks (Chain-of-Thought)
- Optimizing prompt costs (token reduction)
Core Techniques
1. Structural Prompting
Organize prompts clearly using Markdown or XML tags.
<context>
You are a senior specialized in Python.
</context>
<instruction>
Review this code for security vulnerabilities.
</instruction>
<output_format>
JSON format: { "vulnerabilities": [] }
</output_format>
2. Reasoning Patterns
Chain-of-Thought (CoT) Force the model to "think" before answering.
- Zero-Shot CoT: Append "Let's think step by step."
- Few-Shot CoT: Provide examples of the reasoning process.
Few-Shot Learning Don't just tell; show.
Input: "The sky is blue" -> Sentiment: Neutral
Input: "I love this tool" -> Sentiment: Positive
Input: "This is garbage" ->
3. Hallucination Mitigation
- "Answer only from the context": Restrict knowledge source.
- "If you don't know, say 'I don't know'": Prevent guessing.
- Citation Requirement: Ask the model to quote the source text.
Persona Design
Define:
- Identity: "You are a Senior SRE Engineer..."
- Tone: "Professional, concise, direct."
- Constraints: "Do not apologize. Do not use filler words."
Troubleshooting
- Model is lazy? -> Add "Take a deep breath and work on this step-by-step." / "This is critical for the project."
- Model ignores instructions? -> Move critical instructions to the end of the prompt (Recency Bias).