Agentic DesignPatterns and tools for production AI agents

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Chain-of-Thought(CoT)

Breaks down complex problems into step-by-step intermediate reasoning steps

Complexity: lowReasoning Techniques

In 30 seconds

What: Prompts the model to show intermediate reasoning steps before reaching a final answer, making the solution path explicit.
When to use: Problems requiring multi-step logic, math, or causal reasoning where showing work helps catch errors and improves accuracy.
Watch out: Longer reasoning can amplify early mistakes; each step compounds errors from prior steps, degrading final answers.

Video Explanation

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Agentic DesignPatterns and tools for production AI agents

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🔗

Chain-of-Thought(CoT)

Breaks down complex problems into step-by-step intermediate reasoning steps

Complexity: lowReasoning Techniques

In 30 seconds

What: Prompts the model to show intermediate reasoning steps before reaching a final answer, making the solution path explicit.
When to use: Problems requiring multi-step logic, math, or causal reasoning where showing work helps catch errors and improves accuracy.
Watch out: Longer reasoning can amplify early mistakes; each step compounds errors from prior steps, degrading final answers.

Video Explanation

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30-Second Overview

Pattern: Generate explicit step-by-step reasoning before final answer

Why: Makes reasoning transparent, improves accuracy on complex problems, enables error detection

Key Insight: Add 'Let's think step by step' trigger + numbered reasoning steps → 2-5x accuracy gain

Quick Implementation

1Prompt Setup:Add "Let's think step by step" trigger

2Examples:Provide 2-3 reasoning examples

3Structure:Number each reasoning step clearly

4Verify:Check logical flow & calculations

5Conclude:State final answer explicitly

Example: Problem → Step 1: [reasoning] → Step 2: [reasoning] → Answer: [result]

Do's & Don'ts

✅Use consistent step numbering (Step 1, Step 2, etc.)

✅Include verification steps for calculations

✅Provide diverse few-shot examples

✅Break complex problems into sub-problems

✅Use clear reasoning triggers ("Let's think...")

❌Skip intermediate steps for "obvious" calculations

❌Use CoT for simple, direct questions

❌Ignore token cost increase (2-5x overhead)

❌Make reasoning steps too verbose or redundant

❌Forget to validate logical consistency

When to Use

Use When

• Complex mathematical problems
• Multi-step logical reasoning
• Decision-making with trade-offs
• Educational/explainable AI
• When accuracy > speed

Avoid When

• Simple factual questions
• Real-time/low-latency needs
• Creative/subjective tasks
• Tight token budgets
• When implicit reasoning works

Key Metrics

Reasoning Accuracy

Correct final answers vs baseline

Step Correctness

Valid intermediate reasoning steps

Token Efficiency

Accuracy gain per token increase

Interpretability

Human rating of reasoning clarity

Consistency Rate

Agreement across multiple chains

Error Attribution

Ability to identify wrong steps

Top Use Cases

Math Word Problems: "If a train travels 60 mph for 2 hours..." → step-by-step calculation

Logic Puzzles: Multi-conditional problems requiring systematic elimination

Code Debugging: Trace execution path step-by-step to find errors

Financial Analysis: Break down ROI calculations with intermediate steps

Medical Diagnosis: Systematic symptom analysis → differential diagnosis

References & Further Reading

Deepen your understanding with these curated resources

Academic Papers

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)

Self-Consistency Improves Chain of Thought Reasoning (Wang et al., 2022)

Least-to-Most Prompting Enables Complex Reasoning (Zhou et al., 2022)

Large Language Models are Zero-Shot Reasoners (Kojima et al., 2022)

Implementation Guides

Anthropic Claude Chain of Thought Best Practices

Google AI: Chain-of-Thought Prompting Guide

Prompt Engineering Guide - CoT Techniques

Tools & Libraries

LangChain CoT Chains Implementation

DSPy Framework for Programmatic CoT

Guidance Library for Structured Reasoning

OpenAI API with Structured Outputs for CoT

Community & Discussions

OpenAI Developer Forum - Prompt Engineering

LangChain Discord Community

r/MachineLearning - Chain of Thought discussions

Anthropic Discord - Claude Developers

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Patterns

Design Patterns & Techniques

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30-Second Overview

Pattern: Generate explicit step-by-step reasoning before final answer

Why: Makes reasoning transparent, improves accuracy on complex problems, enables error detection

Key Insight: Add 'Let's think step by step' trigger + numbered reasoning steps → 2-5x accuracy gain

Quick Implementation

1Prompt Setup:Add "Let's think step by step" trigger

2Examples:Provide 2-3 reasoning examples

3Structure:Number each reasoning step clearly

4Verify:Check logical flow & calculations

5Conclude:State final answer explicitly

Example: Problem → Step 1: [reasoning] → Step 2: [reasoning] → Answer: [result]

Do's & Don'ts

✅Use consistent step numbering (Step 1, Step 2, etc.)

✅Include verification steps for calculations

✅Provide diverse few-shot examples

✅Break complex problems into sub-problems

✅Use clear reasoning triggers ("Let's think...")

❌Skip intermediate steps for "obvious" calculations

❌Use CoT for simple, direct questions

❌Ignore token cost increase (2-5x overhead)

❌Make reasoning steps too verbose or redundant

❌Forget to validate logical consistency

When to Use

Use When

• Complex mathematical problems
• Multi-step logical reasoning
• Decision-making with trade-offs
• Educational/explainable AI
• When accuracy > speed

Avoid When

• Simple factual questions
• Real-time/low-latency needs
• Creative/subjective tasks
• Tight token budgets
• When implicit reasoning works

Key Metrics

Reasoning Accuracy

Correct final answers vs baseline

Step Correctness

Valid intermediate reasoning steps

Token Efficiency

Accuracy gain per token increase

Interpretability

Human rating of reasoning clarity

Consistency Rate

Agreement across multiple chains

Error Attribution

Ability to identify wrong steps

Top Use Cases

Math Word Problems: "If a train travels 60 mph for 2 hours..." → step-by-step calculation

Logic Puzzles: Multi-conditional problems requiring systematic elimination

Code Debugging: Trace execution path step-by-step to find errors

Financial Analysis: Break down ROI calculations with intermediate steps

Medical Diagnosis: Systematic symptom analysis → differential diagnosis

References & Further Reading

Deepen your understanding with these curated resources

Academic Papers

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)

Self-Consistency Improves Chain of Thought Reasoning (Wang et al., 2022)

Least-to-Most Prompting Enables Complex Reasoning (Zhou et al., 2022)

Large Language Models are Zero-Shot Reasoners (Kojima et al., 2022)

Implementation Guides

Anthropic Claude Chain of Thought Best Practices

Google AI: Chain-of-Thought Prompting Guide

Prompt Engineering Guide - CoT Techniques

Tools & Libraries

LangChain CoT Chains Implementation

DSPy Framework for Programmatic CoT

Guidance Library for Structured Reasoning

OpenAI API with Structured Outputs for CoT

Community & Discussions

OpenAI Developer Forum - Prompt Engineering

LangChain Discord Community

r/MachineLearning - Chain of Thought discussions

Anthropic Discord - Claude Developers

Chain-of-Thought (CoT) - Agentic Design