Chain-of-Thought vs Direct Prediction

Choose a Problem Type:

🧮

Math Problem

Multi-step calculation

🧩

Logic Puzzle

Deductive reasoning

🗺️

Planning Task

Sequential decision-making

📊

Data Analysis

Interpretation and insight

❌ Often Wrong

If a train travels 60 mph for 2.5 hours, then 80 mph for 1.5 hours, what is the total distance?

🤔

Model immediately tries to predict the answer...

"Based on similar problems in training data..."

Direct Answer:

270 miles

(Often incorrect due to pattern matching)

✅ Usually Correct

If a train travels 60 mph for 2.5 hours, then 80 mph for 1.5 hours, what is the total distance?

Step 1: Break down the problem

I need to calculate distance for two separate segments and add them together.

Step 2: First segment

Distance = Speed × Time = 60 mph × 2.5 hours = 150 miles

Step 3: Second segment

Distance = Speed × Time = 80 mph × 1.5 hours = 120 miles

Step 4: Total distance

Total = 150 miles + 120 miles = 270 miles

Reasoned Answer:

270 miles

(Correct through step-by-step reasoning)

🎯 Accuracy Improvement

Chain-of-thought dramatically improves accuracy on complex problems by enabling error detection and correction at each step.

🔍 Transparency

We can see exactly how the model arrives at its answer, making it easier to identify and fix reasoning errors.

🧠 Decomposition

Complex problems are broken into manageable steps, mirroring how humans approach difficult tasks.

⚡ Computational Cost

Chain-of-thought uses more tokens and computation but provides much better results for reasoning tasks.