Zero-Shot, Few-Shot, and Chain-of-Thought: The Three Fundamental Prompting Strategies

import anthropic

client = anthropic.Anthropic()

def build_few_shot_cot_prompt(
    task_description: str,
    examples: list[dict],  # each dict: {"input": str, "reasoning": str, "output": str}
    query: str
) -> str:
    """
    Builds a few-shot chain-of-thought prompt dynamically.

    examples: list of {"input": ..., "reasoning": ..., "output": ...}
    """
    prompt_parts = [task_description, ""]

    for i, ex in enumerate(examples, 1):
        prompt_parts.append(f"Example {i}:")
        prompt_parts.append(f"Input: {ex['input']}")
        prompt_parts.append(f"Reasoning: {ex['reasoning']}")
        prompt_parts.append(f"Output: {ex['output']}")
        prompt_parts.append("")

    prompt_parts.append("Now process this input:")
    prompt_parts.append(f"Input: {query}")
    prompt_parts.append("Reasoning:")  # model continues from here

    return "\n".join(prompt_parts)


# ── Example: customer priority classification ─────────────────────────────────
task = "Classify customer support messages as Urgent, Normal, or Low priority. Show reasoning first."

examples = [
    {
        "input": "The payment gateway is throwing 500 errors and we can't process any orders",
        "reasoning": "Core business function (payments) is completely broken. Revenue impact is immediate. No workaround exists.",
        "output": "Urgent"
    },
    {
        "input": "How do I export my data to CSV?",
        "reasoning": "Feature/how-to question with no urgency signal. Can be answered asynchronously.",
        "output": "Low"
    },
    {
        "input": "Our trial ends in 2 days and I need a quick decision from my manager — can we extend it?",
        "reasoning": "Time-sensitive (2 days) but not blocking current work. Sales/retention concern, not technical.",
        "output": "Normal"
    },
]

query = "We're giving a product demo in 45 minutes and the dashboard isn't loading for our client"

prompt = build_few_shot_cot_prompt(task, examples, query)

response = client.messages.create(
    model="claude-sonnet-4-5",
    max_tokens=500,
    temperature=0,  # deterministic for classification tasks
    messages=[{"role": "user", "content": prompt}]
)

print(response.content[0].text)
# Expected output:
# Reasoning: Hard deadline (45 min) with client-facing consequences.
# Technical issue (dashboard not loading) during a high-stakes sales situation.
# Reputational and sales risk if not resolved immediately.
# Output: Urgent