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    Lesson 2 of 8

    Chain-of-Thought & Reasoning Techniques

    From the course Advanced Prompt Engineering: Master AI Communication

    Chain-of-thought (CoT) prompting is one of the most powerful techniques for improving AI reasoning accuracy. Instead of asking for a direct answer, you instruct the model to show its work.

    Standard Chain-of-Thought

    Add "Let's think step by step" or "Think through this carefully before answering" to your prompt. This simple addition can dramatically improve accuracy on math, logic, and multi-step reasoning tasks.

    ❌ "What is 17 × 24 + 156 ÷ 12?" ✅ "What is 17 × 24 + 156 ÷ 12? Show your work step by step."

    Structured Reasoning Frameworks

    For complex analysis, provide an explicit reasoning framework:

    "Analyze this business decision using the following framework:

    1. SITUATION: What are the key facts?
    2. COMPLICATIONS: What makes this difficult?
    3. OPTIONS: What are the possible approaches?
    4. RECOMMENDATION: What do you suggest and why?
    5. RISKS: What could go wrong?"

    Self-Consistency

    Generate multiple reasoning paths and take the majority answer. You can do this in a single prompt:

    "Solve this problem three different ways. For each approach, show your reasoning. Then compare the answers and provide the most likely correct one."

    Tree-of-Thought

    For problems with many possible solution paths:

    "Consider three different approaches to this problem. For each:

    1. Describe the approach
    2. Work through the first few steps
    3. Evaluate: is this approach promising? Why or why not? Then select the most promising approach and complete it."

    Metacognitive Prompting

    Ask the model to evaluate its own confidence:

    "After providing your answer, rate your confidence (1-10) and explain what assumptions you made that could be wrong."

    This helps you identify when the model is uncertain and when to verify its output.

    When to Use Chain-of-Thought

    • Complex math or logic problems
    • Multi-step reasoning (cause and effect analysis)
    • Decisions requiring weighing multiple factors
    • Tasks where accuracy matters more than speed

    Note: CoT increases token usage and latency. For simple factual lookups or creative generation, it adds overhead without much benefit.

    Key Takeaways

    • •Chain-of-thought prompting improves accuracy by making the model show its work
    • •Structured reasoning frameworks guide analysis through explicit steps
    • •Self-consistency: generate multiple reasoning paths and compare answers
    • •Tree-of-thought explores multiple solution approaches before committing
    • •Use CoT for complex reasoning; skip it for simple factual or creative tasks
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