Advanced Prompting Techniques

from openai import OpenAI


def zero_shot_cot(problem: str) -> str:
    """Apply zero-shot chain-of-thought prompting.
    
    The magic phrase 'Let's think step by step' was discovered by Kojima et al.
    (2022) to be surprisingly effective at triggering reasoning behavior -- even
    without providing any examples. This is the lowest-effort, highest-impact
    prompting upgrade you can make.
    """
    client = OpenAI()
    
    # The trailing instruction is the key -- it tells the model to decompose
    # the problem rather than jumping straight to an answer
    prompt = f"""{problem}

Let's think step by step."""
    
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    
    return response.choices[0].message.content


# Usage
problem = """
If a train travels at 60 mph for 2 hours, then at 80 mph for 3 hours,
what is the average speed for the entire journey?
"""

solution = zero_shot_cot(problem)
print(solution)

from openai import OpenAI


def few_shot_cot(problem: str, examples: list[dict]) -> str:
    """Apply few-shot chain-of-thought with examples.
    
    Few-shot CoT is like training a new employee by walking through solved
    examples before handing them a new problem. The examples teach the model
    the STYLE of reasoning you want, not just the format of the answer.
    
    Production tip: The quality of your examples matters more than quantity.
    Two well-chosen examples that demonstrate edge cases will outperform
    five trivial ones.
    """
    client = OpenAI()
    
    # Build prompt with examples -- each example shows the reasoning chain,
    # teaching the model HOW to think, not just what to output
    prompt_parts = []
    
    for example in examples:
        prompt_parts.append(f"Problem: {example['problem']}")
        prompt_parts.append(f"Reasoning: {example['reasoning']}")
        prompt_parts.append(f"Answer: {example['answer']}\n")
    
    prompt_parts.append(f"Problem: {problem}")
    prompt_parts.append("Reasoning:")
    
    prompt = "\n".join(prompt_parts)
    
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    
    return response.choices[0].message.content


# Usage
examples = [
    {
        "problem": "Roger has 5 tennis balls. He buys 2 more cans of 3 balls each. How many does he have?",
        "reasoning": "Roger started with 5 balls. 2 cans of 3 balls = 6 balls. 5 + 6 = 11 balls.",
        "answer": "11"
    },
    {
        "problem": "A store has 15 apples. If they sell 6 and receive 10 more, how many do they have?",
        "reasoning": "Started with 15 apples. Sold 6, so 15 - 6 = 9. Received 10 more, so 9 + 10 = 19.",
        "answer": "19"
    }
]

problem = "A farmer has 23 cows. He sells 7 and buys 12 more. How many cows does he have?"
solution = few_shot_cot(problem, examples)
print(solution)

from openai import OpenAI


def self_ask_cot(question: str) -> str:
    """Use self-ask prompting for multi-hop reasoning."""
    client = OpenAI()
    
    system = """You are a reasoning assistant that breaks down complex questions.
When given a question, identify if follow-up questions are needed to answer it.
Format your response as:
- Follow-up: <question> (if needed)
- Intermediate answer: <answer>
- Repeat until you can answer the original question
- Final answer: <answer>"""
    
    prompt = f"""Question: {question}

Are follow-up questions needed here?"""
    
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": system},
            {"role": "user", "content": prompt}
        ]
    )
    
    return response.choices[0].message.content


# Usage
question = """
Who was president of the United States when the first iPhone was released,
and what was that president's age at the time?
"""

answer = self_ask_cot(question)
print(answer)

from openai import OpenAI
from dataclasses import dataclass
import json


@dataclass
class ThoughtNode:
    """A node in the thought tree."""
    thought: str
    evaluation: float
    children: list = None
    
    def __post_init__(self):
        if self.children is None:
            self.children = []


class TreeOfThought:
    """Implement Tree-of-Thought reasoning."""
    
    def __init__(self, model: str = "gpt-4o"):
        self.client = OpenAI()
        self.model = model
    
    def generate_thoughts(
        self,
        problem: str,
        context: str = "",
        num_thoughts: int = 3
    ) -> list[str]:
        """Generate multiple distinct thoughts/approaches."""
        prompt = f"""Problem: {problem}

{f"Current progress: {context}" if context else ""}

Generate {num_thoughts} distinct approaches or next steps to solve this problem.
Each approach should be different and explore a unique angle.

Return as JSON: {{"thoughts": ["thought1", "thought2", ...]}}"""
        
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": prompt}],
            response_format={"type": "json_object"}
        )
        
        data = json.loads(response.choices[0].message.content)
        return data.get("thoughts", [])
    
    def evaluate_thought(
        self,
        problem: str,
        thought: str
    ) -> float:
        """Evaluate how promising a thought is (0-1 scale)."""
        prompt = f"""Problem: {problem}

Proposed approach: {thought}

Evaluate this approach on a scale of 0.0 to 1.0:
- 1.0 = Definitely leads to the solution
- 0.5 = Might help, uncertain
- 0.0 = Dead end, won't help

Return JSON: {{"score": <float>, "reasoning": "<brief explanation>"}}"""
        
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": prompt}],
            response_format={"type": "json_object"}
        )
        
        data = json.loads(response.choices[0].message.content)
        return data.get("score", 0.5)
    
    def solve(
        self,
        problem: str,
        depth: int = 3,
        breadth: int = 3,
        threshold: float = 0.3
    ) -> str:
        """Solve using tree-of-thought search."""
        # Generate initial thoughts
        initial_thoughts = self.generate_thoughts(problem, num_thoughts=breadth)
        
        # Create root nodes
        candidates = []
        for thought in initial_thoughts:
            score = self.evaluate_thought(problem, thought)
            if score >= threshold:
                candidates.append(ThoughtNode(thought=thought, evaluation=score))
        
        # Explore best candidates
        for _ in range(depth - 1):
            if not candidates:
                break
            
            # Sort by evaluation and take top candidates
            candidates.sort(key=lambda x: x.evaluation, reverse=True)
            best = candidates[:breadth]
            
            new_candidates = []
            for node in best:
                # Generate follow-up thoughts
                follow_ups = self.generate_thoughts(
                    problem,
                    context=node.thought,
                    num_thoughts=breadth
                )
                
                for thought in follow_ups:
                    combined = f"{node.thought}\nThen: {thought}"
                    score = self.evaluate_thought(problem, combined)
                    
                    if score >= threshold:
                        child = ThoughtNode(thought=combined, evaluation=score)
                        node.children.append(child)
                        new_candidates.append(child)
            
            candidates = new_candidates
        
        # Get best final thought
        if candidates:
            best = max(candidates, key=lambda x: x.evaluation)
            return self._generate_final_answer(problem, best.thought)
        
        return "Could not find a solution path."
    
    def _generate_final_answer(self, problem: str, reasoning: str) -> str:
        """Generate final answer based on best reasoning path."""
        prompt = f"""Problem: {problem}

Reasoning path:
{reasoning}

Based on this reasoning, provide the final answer."""
        
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": prompt}]
        )
        
        return response.choices[0].message.content


# Usage
tot = TreeOfThought()

problem = """
You have 4 numbers: 2, 3, 4, 5
Using each number exactly once and any arithmetic operations (+, -, *, /),
create an expression that equals 24.
"""

solution = tot.solve(problem, depth=3, breadth=3)
print(solution)

from openai import OpenAI
import json
import re


class ReActAgent:
    """Agent using ReAct pattern for reasoning and action."""
    
    def __init__(self, tools: dict, model: str = "gpt-4o"):
        self.client = OpenAI()
        self.model = model
        self.tools = tools  # name -> function mapping
        
        self.tool_descriptions = self._format_tools()
    
    def _format_tools(self) -> str:
        """Format tool descriptions for the prompt."""
        lines = []
        for name, func in self.tools.items():
            doc = func.__doc__ or "No description"
            lines.append(f"- {name}: {doc}")
        return "\n".join(lines)
    
    def run(self, task: str, max_steps: int = 10) -> str:
        """Run the ReAct loop."""
        system = f"""You are an agent that solves tasks through reasoning and actions.

Available tools:
{self.tool_descriptions}

For each step, respond with exactly one of:
1. Thought: <your reasoning about what to do next>
2. Action: <tool_name>(<args>)
3. Answer: <final answer when task is complete>

Always start with a Thought, then take an Action if needed, observe the result,
and continue until you can provide the Answer."""
        
        messages = [
            {"role": "system", "content": system},
            {"role": "user", "content": f"Task: {task}"}
        ]
        
        trajectory = []
        
        for step in range(max_steps):
            response = self.client.chat.completions.create(
                model=self.model,
                messages=messages
            )
            
            output = response.choices[0].message.content.strip()
            trajectory.append(output)
            
            # Check for final answer
            if output.startswith("Answer:"):
                return output[7:].strip()
            
            # Parse and execute action
            if "Action:" in output:
                action_match = re.search(r'Action:\s*(\w+)\((.*?)\)', output)
                if action_match:
                    tool_name = action_match.group(1)
                    args_str = action_match.group(2)
                    
                    # Execute tool
                    if tool_name in self.tools:
                        try:
                            # Parse arguments
                            if args_str:
                                args = [a.strip().strip('"\'') for a in args_str.split(",")]
                                result = self.tools[tool_name](*args)
                            else:
                                result = self.tools[tool_name]()
                            
                            observation = f"Observation: {result}"
                        except Exception as e:
                            observation = f"Observation: Error - {str(e)}"
                    else:
                        observation = f"Observation: Unknown tool '{tool_name}'"
                    
                    trajectory.append(observation)
                    
                    messages.append({"role": "assistant", "content": output})
                    messages.append({"role": "user", "content": observation})
                else:
                    messages.append({"role": "assistant", "content": output})
                    messages.append({
                        "role": "user",
                        "content": "Please format your action as: Action: tool_name(args)"
                    })
            else:
                messages.append({"role": "assistant", "content": output})
                messages.append({
                    "role": "user",
                    "content": "Continue with your next thought or action."
                })
        
        return "Max steps reached. Trajectory:\n" + "\n".join(trajectory)


# Example tools
def search(query: str) -> str:
    """Search for information on the web."""
    # Simulated search results
    return f"Search results for '{query}': Found relevant information about the topic."

def calculate(expression: str) -> str:
    """Evaluate a mathematical expression."""
    try:
        result = eval(expression)
        return str(result)
    except Exception as e:
        return f"Error: {e}"

def lookup(topic: str) -> str:
    """Look up a specific fact or definition."""
    facts = {
        "python": "Python is a high-level programming language created by Guido van Rossum.",
        "earth radius": "Earth's radius is approximately 6,371 kilometers.",
    }
    return facts.get(topic.lower(), f"No information found for '{topic}'")


# Usage
agent = ReActAgent(tools={
    "search": search,
    "calculate": calculate,
    "lookup": lookup
})

result = agent.run(
    "What is the circumference of Earth? Calculate it using the radius."
)
print(result)

from openai import OpenAI
from collections import Counter
import json


class SelfConsistency:
    """Apply self-consistency decoding."""
    
    def __init__(self, model: str = "gpt-4o"):
        self.client = OpenAI()
        self.model = model
    
    def solve(
        self,
        problem: str,
        num_samples: int = 5,
        temperature: float = 0.7
    ) -> dict:
        """Solve using self-consistency."""
        # Generate multiple reasoning paths
        responses = []
        
        prompt = f"""{problem}

Think through this step by step and provide your answer.
At the end, clearly state your final answer after "Final Answer:".
"""
        
        for _ in range(num_samples):
            response = self.client.chat.completions.create(
                model=self.model,
                messages=[{"role": "user", "content": prompt}],
                temperature=temperature
            )
            
            text = response.choices[0].message.content
            responses.append(text)
        
        # Extract final answers
        answers = []
        for response in responses:
            answer = self._extract_answer(response)
            if answer:
                answers.append(answer)
        
        # Find most common answer
        if answers:
            counter = Counter(answers)
            most_common = counter.most_common(1)[0]
            
            return {
                "answer": most_common[0],
                "confidence": most_common[1] / len(answers),
                "num_samples": num_samples,
                "all_answers": dict(counter),
                "sample_reasoning": responses[0]
            }
        
        return {
            "answer": None,
            "confidence": 0,
            "num_samples": num_samples,
            "error": "Could not extract consistent answer"
        }
    
    def _extract_answer(self, response: str) -> str:
        """Extract the final answer from a response."""
        # Look for "Final Answer:" pattern
        if "Final Answer:" in response:
            answer = response.split("Final Answer:")[-1].strip()
            # Clean up the answer
            answer = answer.split("\n")[0].strip()
            return answer
        
        # Fallback: last line
        lines = [l.strip() for l in response.split("\n") if l.strip()]
        if lines:
            return lines[-1]
        
        return None


# Usage
sc = SelfConsistency()

problem = """
A farmer has 17 sheep. All but 9 run away. How many sheep are left?
"""

result = sc.solve(problem, num_samples=5)
print(f"Answer: {result['answer']}")
print(f"Confidence: {result['confidence']:.0%}")
print(f"Vote distribution: {result['all_answers']}")

from openai import OpenAI
from dataclasses import dataclass
from typing import Callable, Any


@dataclass
class ChainStep:
    """A step in a prompt chain."""
    name: str
    prompt_template: str
    parser: Callable[[str], Any] = None


class PromptChain:
    """Chain multiple prompts together."""
    
    def __init__(self, model: str = "gpt-4o"):
        self.client = OpenAI()
        self.model = model
        self.steps: list[ChainStep] = []
    
    def add_step(
        self,
        name: str,
        prompt_template: str,
        parser: Callable[[str], Any] = None
    ):
        """Add a step to the chain."""
        self.steps.append(ChainStep(
            name=name,
            prompt_template=prompt_template,
            parser=parser
        ))
        return self
    
    def run(self, initial_input: dict) -> dict:
        """Run the prompt chain."""
        context = initial_input.copy()
        results = {}
        
        for step in self.steps:
            # Format prompt with current context
            prompt = step.prompt_template.format(**context)
            
            response = self.client.chat.completions.create(
                model=self.model,
                messages=[{"role": "user", "content": prompt}]
            )
            
            output = response.choices[0].message.content
            
            # Parse if parser provided
            if step.parser:
                output = step.parser(output)
            
            results[step.name] = output
            context[step.name] = output
        
        return results


# Usage: Research and summarization chain
chain = PromptChain()

chain.add_step(
    name="outline",
    prompt_template="""Create an outline for an article about: {topic}

Include 3-5 main sections with brief descriptions.
Format as a numbered list."""
)

chain.add_step(
    name="draft",
    prompt_template="""Based on this outline:
{outline}

Write a detailed first draft of the article about {topic}.
Expand each section with relevant information and examples."""
)

chain.add_step(
    name="review",
    prompt_template="""Review this draft and identify improvements:
{draft}

List:
1. Factual issues or gaps
2. Clarity improvements
3. Missing information
4. Structural suggestions"""
)

chain.add_step(
    name="final",
    prompt_template="""Improve this draft based on the review:

Original draft:
{draft}

Review feedback:
{review}

Write the improved final version."""
)

results = chain.run({"topic": "the future of renewable energy"})
print("Final article:")
print(results["final"])

from openai import OpenAI


class LeastToMost:
    """Implement least-to-most prompting."""
    
    def __init__(self, model: str = "gpt-4o"):
        self.client = OpenAI()
        self.model = model
    
    def decompose(self, problem: str) -> list[str]:
        """Decompose problem into subproblems."""
        prompt = f"""Problem: {problem}

Break this problem down into simpler subproblems that can be solved sequentially.
List them from simplest to most complex, where each builds on previous answers.

Format as a numbered list of subproblems."""
        
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": prompt}]
        )
        
        text = response.choices[0].message.content
        
        # Parse numbered list
        subproblems = []
        for line in text.split("\n"):
            line = line.strip()
            if line and line[0].isdigit():
                # Remove number and punctuation
                subproblem = line.lstrip("0123456789.):- ").strip()
                if subproblem:
                    subproblems.append(subproblem)
        
        return subproblems
    
    def solve(self, problem: str) -> dict:
        """Solve using least-to-most approach."""
        # Decompose
        subproblems = self.decompose(problem)
        
        # Solve sequentially
        solutions = []
        context = f"Original problem: {problem}\n\n"
        
        for i, subproblem in enumerate(subproblems):
            prompt = f"""{context}
Previous solutions:
{chr(10).join(f"- {s['subproblem']}: {s['solution']}" for s in solutions)}

Now solve: {subproblem}

Provide a clear, concise solution."""
            
            response = self.client.chat.completions.create(
                model=self.model,
                messages=[{"role": "user", "content": prompt}]
            )
            
            solution = response.choices[0].message.content
            solutions.append({
                "subproblem": subproblem,
                "solution": solution
            })
        
        # Final synthesis
        synthesis_prompt = f"""Original problem: {problem}

Solutions to subproblems:
{chr(10).join(f"{i+1}. {s['subproblem']}: {s['solution']}" for i, s in enumerate(solutions))}

Now provide the complete final answer to the original problem."""
        
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": synthesis_prompt}]
        )
        
        return {
            "subproblems": subproblems,
            "solutions": solutions,
            "final_answer": response.choices[0].message.content
        }


# Usage
l2m = LeastToMost()

problem = """
Design a system to recommend movies to users based on their watching history
and preferences. The system should handle millions of users and movies.
"""

result = l2m.solve(problem)

print("Subproblems:")
for i, sp in enumerate(result["subproblems"], 1):
    print(f"  {i}. {sp}")

print("\nFinal Answer:")
print(result["final_answer"])