# Force JSON output by prefilling with opening brace
response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Extract: name, age, city from: 'John is 28 years old and lives in Mumbai'"},
        {"role": "assistant", "content": "{"},   # ← prefill forces JSON start
    ]
)
# Model MUST continue the JSON: {"name": "John", "age": 28, "city": "Mumbai"}
result = "{" + response.content[0].text   # prepend the "{" we used as prefill

# Force numbered list format
messages=[
    {"role": "user",      "content": "List 5 benefits of RAG"},
    {"role": "assistant", "content": "1."},   # ← forces numbered list
]

# Zero-Shot CoT — just add "Think step by step"
prompt = f"""
{question}

Think step by step before giving your final answer.
"""

# CoT with scratchpad — separate reasoning from answer
prompt = f"""
{question}

First, reason through this carefully in a <scratchpad> tag.
Then give your final answer in an <answer> tag.
"""

# Parse out just the answer (not the reasoning)
import re
response_text = response.content[0].text
answer_match = re.search(r'<answer>(.*?)</answer>', response_text, re.DOTALL)
if answer_match:
    answer = answer_match.group(1).strip()

# CoT for classification — "Explain your reasoning, then classify"
system = """Analyze the given text. First explain your reasoning in 1-2 sentences.
Then output exactly one of: POSITIVE / NEGATIVE / NEUTRAL on a new line."""

# Self-consistency — run same prompt N times, take majority vote
from collections import Counter

def classify_with_consistency(text: str, n: int = 5) -> str:
    results = []
    for _ in range(n):
        response = client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=10,
            temperature=0.3,   # slight variation per run
            messages=[{"role": "user", "content": f'Classify: {text}'}]
        )
        results.append(response.content[0].text.strip())
    most_common = Counter(results).most_common(1)[0]
    return most_common[0]   # most frequent answer

# Verify-and-correct — ask model to check its own work
async def verified_extraction(text: str) -> dict:
    # Step 1: extract
    extraction = await extract(text)

    # Step 2: verify
    verify_prompt = f"""Check if this extraction is accurate and complete.

Original text: {text}
Extracted data: {extraction}

Is anything missing, incorrect, or hallucinated?
If correct, respond: VERIFIED
If issues found, respond: ISSUES: [describe what's wrong]"""
    verification = await call_claude(verify_prompt)

    if "ISSUES:" in verification:
        # Step 3: re-extract with the issues identified
        return await extract_with_context(text, verification)
    return extraction