Chain of Thought Reasoning
Why Planning Matters
The Problem: LLMs often fail on complex tasks when asked to jump directly to an answer. They make reasoning errors and miss important steps.
The Solution: Chain of Thought (CoT) prompting encourages the model to think step-by-step, dramatically improving accuracy on reasoning-intensive tasks.
Real Impact: CoT can improve math problem accuracy from 18% to 97% and enables agents to tackle multi-step planning tasks reliably.
Real-World Analogy
Think of CoT like showing your work on a math test:
- Direct Answer = Writing just "42" -- might be wrong, hard to debug
- Chain of Thought = Writing each step -- easier to verify and correct
- Task Decomposition = Breaking a big problem into smaller solvable parts
- Tree of Thoughts = Exploring multiple solution paths simultaneously
CoT Techniques
Zero-Shot CoT
Simply add "Think step by step" to your prompt. Surprisingly effective for many reasoning tasks.
Few-Shot CoT
Provide examples with detailed reasoning chains. The model learns to mirror the reasoning style.
Self-Consistency
Generate multiple reasoning paths and take the majority vote. Reduces errors from any single chain.
Plan-and-Execute
First create a complete plan, then execute each step. Separates planning from execution.
Zero-Shot CoT
# Zero-Shot Chain of Thought
response = client.chat.completions.create(
model="gpt-4o",
messages=[{
"role": "user",
"content": "A store has 3 types of fruit. Apples cost $2, bananas $1, oranges $3. I buy 4 apples, 6 bananas, and 2 oranges. How much do I spend? Think step by step."
}]
)
# Model breaks down: 4*2=8, 6*1=6, 2*3=6, total=20
# Plan-and-Execute Pattern
class PlanAndExecute:
def run(self, task):
# Step 1: Create plan
plan = self.planner.create_plan(task)
# Step 2: Execute each step
results = []
for step in plan.steps:
result = self.executor.execute(step, results)
results.append(result)
# Step 3: Synthesize final answer
return self.synthesizer.combine(results)Task Decomposition
Planning Strategies
| Strategy | How It Works | Best For |
|---|---|---|
| Linear Plan | Sequential step list | Well-defined workflows |
| DAG Plan | Steps with dependencies | Parallelizable tasks |
| Adaptive Plan | Replan after each step | Uncertain environments |
| Hierarchical | High-level then detailed | Very complex tasks |
Tree of Thoughts
Tree of Thoughts (ToT)
- Generate: Create multiple candidate reasoning steps
- Evaluate: Score each candidate for promise
- Expand: Continue the most promising branches
- Backtrack: Abandon dead-end branches and try alternatives
Quick Reference
| Technique | Description | Accuracy Gain |
|---|---|---|
| Zero-Shot CoT | "Think step by step" | +20-40% |
| Few-Shot CoT | Examples with reasoning chains | +30-50% |
| Self-Consistency | Multiple paths, majority vote | +5-15% over CoT |
| Tree of Thoughts | Branch and evaluate paths | Best for creative tasks |
| Plan-and-Execute | Separate planning from doing | Best for multi-step tasks |