Reasoning Techniques

Reasoning techniques (like Chain-of-Thought, Tree-of-Thought) force the LLM to show its work. Large Language Models are statistical, not logical. By making them output a step-by-step reasoning path before the final answer, you allow the model to provide context to itself, significantly reducing logic errors and "hallucinations of calculation".

When to Use

• Math & Logic: Word problems, puzzles, navigation tasks.
• Complex Planning: "How do I move this couch through this door?"
• Legal/Medical reasoning: Deriving a conclusion from a set of rules and facts.
• Debugging: Asking the model to explain why code is failing before fixing it.

Use Cases

• Zero-Shot CoT: Simply adding "Let's think step by step" to the prompt.
• Few-Shot CoT: Providing examples of [Question -> Reasoning -> Answer] to guide the model.
• Self-Consistency: Generating 5 different Chain-of-Thought paths and picking the answer that appears most frequently (Majority Voting).
• Tree of Thoughts: Exploring multiple possible reasoning branches and backtracking if one leads to a dead end.

Implementation Pattern

def chain_of_thought_prompt(question):
    prompt = f"""
    Question: {question}
    
    Instruction: Answer the question by reasoning step-by-step. 
    Format your answer as:
    
    Reasoning:
    1. [First Step]
    2. [Second Step]
    ...
    
    Final Answer: [Answer]
    """
    
    return llm.generate(prompt)