luokai0 Prompt Engineering Expert
Before Starting
- What model are you prompting — GPT-4, Claude, Gemini, local?
- Task type — extraction, classification, generation, reasoning, coding?
- Consistency requirement — needs structured output or free-form is fine?
Core Expertise Areas
System Prompt Design
System prompt sets the model's persona, constraints, and output format. Be explicit about what to do AND what not to do. Specify the output format exactly — JSON, markdown, plain text. Include examples when behavior is ambiguous. Test system prompt changes on a diverse set of inputs before deploying.
Chain-of-Thought Prompting
Add "Think step by step" or "Let's reason through this" for complex tasks. CoT improves accuracy on math, logic, and multi-step reasoning significantly. Zero-shot CoT: just append "Think step by step." to the user prompt. Few-shot CoT: show 2 to 3 examples with explicit reasoning chains. Self-consistency: run same prompt 5 times, take majority vote answer.
Few-Shot Learning
2 to 5 examples in the prompt often outperform 0-shot dramatically. Examples must be diverse — do not use identical structure. Order matters: hardest examples last, easiest first. Format examples identically to how you want the output. Dynamic few-shot: retrieve the most similar examples to current query.
Structured Output
Ask for JSON directly in the system prompt. Specify exact schema: field names, types, nested objects. Use Pydantic or JSON Schema to validate model output. Retry on parsing failure with error message included in retry prompt. OpenAI function calling / tool use enforces schema more reliably than asking.
Prompt Optimization
A/B test prompts on a diverse eval set before production changes. Measure: accuracy, consistency, output length, latency. DSPy: automatic prompt optimization using gradient-like feedback. Prompt compression: remove redundant words — shorter prompts are cheaper. Temperature: 0 for extraction/classification, 0.7 for creative generation.
Key Patterns
Production System Prompt Template
You are [specific role] for [specific context].
Your task: [exact task description]
Rules:
- [constraint 1]
- [constraint 2]
- NEVER [what to avoid]
Output format:
[exact format specification, JSON schema, or example]
If you are unsure, [fallback behavior].
Few-Shot Classification Prompt
Classify the sentiment of each review as POSITIVE, NEGATIVE, or NEUTRAL.
Review: "The product arrived quickly and works perfectly."
Sentiment: POSITIVE
Review: "Completely broken on arrival. Waste of money."
Sentiment: NEGATIVE
Review: "It is okay, does the job but nothing special."
Sentiment: NEUTRAL
Review: "{user_review}"
Sentiment:
Structured JSON Extraction
from openai import OpenAI
from pydantic import BaseModel
class ExtractedData(BaseModel):
name: str
email: str
company: str | None
intent: str
client = OpenAI()
def extract_contact(text: str) -> ExtractedData:
response = client.beta.chat.completions.parse(
model="gpt-4o-mini",
messages=[
{"role": "system", "content": "Extract contact information from the text."},
{"role": "user", "content": text}
],
response_format=ExtractedData
)
return response.choices[0].message.parsed
Chain-of-Thought for Reasoning
Solve the following problem. Show your full reasoning before giving the answer.
Problem: A store buys items for $60 and sells them with a 40% markup.
After a 15% discount promotion, what is the final selling price?
Reasoning: Let me work through this step by step.
Step 1: Calculate the selling price with markup.
Markup amount = $60 * 0.40 = $24
Selling price = $60 + $24 = $84
Step 2: Apply the 15% discount.
Discount amount = $84 * 0.15 = $12.60
Final price = $84 - $12.60 = $71.40
Answer: $71.40
Best Practices
- Be specific — vague prompts produce vague outputs
- Show examples — they outperform instructions alone almost every time
- Constrain the output format explicitly — saves parsing headaches
- Test on diverse inputs before deploying — edge cases expose prompt brittleness
- Version control your prompts — treat them like code with commits
Common Pitfalls
| Pitfall | Consequence | Fix |
|---|---|---|
| Underspecified prompt | Model guesses and guesses wrong | Add explicit instructions for every edge case |
| No output format specification | Inconsistent structure, broken parsing | Always specify exact format or JSON schema |
| Single example | Model learns wrong pattern | Use 3 to 5 diverse examples |
| Temperature too high for factual tasks | Hallucinations and inconsistency | Use temperature 0 for extraction and classification |
| No eval set | Prompt regressions undetected | Build 50+ test cases before optimizing |
Related Skills
- llm-engineering-expert
- agent-memory-expert
- rag-systems-expert
- mcp-builder-expert