Prompt Engineering — Getting the Best from LLMs

from openai import OpenAI

client = OpenAI()  # requires OPENAI_API_KEY env var

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# 1. ZERO-SHOT: No examples — relies purely on model knowledge
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def zero_shot(text: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a sentiment analysis expert. Respond with only: POSITIVE, NEGATIVE, or NEUTRAL"},
            {"role": "user", "content": f"Classify: '{text}'"}
        ],
        temperature=0,  # deterministic for classification
        max_tokens=10,
    )
    return response.choices[0].message.content.strip()

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# 2. FEW-SHOT: 3-5 examples teach the expected format
# Often boosts accuracy 10-25% over zero-shot
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few_shot_prompt = """Classify the sentiment of these reviews:

Review: "The product quality was amazing!" → POSITIVE
Review: "Delivery took 3 weeks and item was damaged" → NEGATIVE
Review: "It's okay, does what it says" → NEUTRAL

Review: "{text}" → """

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# 3. CHAIN-OF-THOUGHT (CoT) — dramatic improvement for reasoning
# Adding "think step by step" unlocks 100B+ model reasoning ability
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def chain_of_thought(problem: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": "You are a math tutor. Think through problems step by step before giving the final answer."},
            {"role": "user", "content": f"{problem}\n\nLet's think through this step by step:"}
        ],
        temperature=0,
        max_tokens=500,
    )
    return response.choices[0].message.content

# Example: "A store sells apples for $0.75 each. Alice buys 7 apples and pays with $10. How much change does she get?"
# CoT answer walks through: 7 × $0.75 = $5.25 → $10 - $5.25 = $4.75
# Direct answer often fails on multi-step arithmetic; CoT solves it

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# 4. STRUCTURED OUTPUT — JSON mode for reliable parsing
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import json

def extract_entities(text: str) -> dict:
    """Reliably extract structured data from unstructured text."""
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": """Extract entities from the text. 
Respond with ONLY valid JSON:
{"people": [], "organizations": [], "locations": [], "dates": []}"""},
            {"role": "user", "content": text}
        ],
        response_format={"type": "json_object"},  # forces valid JSON output
        temperature=0,
    )
    return json.loads(response.choices[0].message.content)

result = extract_entities("Elon Musk founded SpaceX in 2002 in Hawthorne, California.")
print(json.dumps(result, indent=2))

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# 5. SYSTEM PROMPT ENGINEERING — define the model's persona
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system_prompts = {
    "Code reviewer": """You are a senior software engineer specializing in Python code review.
Analyze code for: bugs, security issues, performance, readability.
Format your response as: ISSUES | SUGGESTIONS | RATING (1-10)""",

    "Medical assistant": """You are a medical information assistant. 
IMPORTANT: Always recommend consulting a qualified doctor.
Provide factual, evidence-based information.
Never diagnose conditions or prescribe treatments.""",

    "Data analyst": """You are a data analyst. 
When given data, always:
1. Identify patterns and trends
2. Highlight anomalies
3. Suggest actionable insights
4. Present findings clearly with bullet points"""
}