AI Reasoning Basics

Introduction

Reasoning is an LLM's ability to break a problem into steps and combine intermediate results into an answer. Modern agents heavily rely on reasoning patterns like Chain-of-Thought, ReAct, Tree-of-Thought, and dedicated reasoning models (o1, o3, Claude 3.7 thinking).

Beginner analogy: ask a student '17 × 24'. A weak one guesses; a good one writes out 17×20 + 17×4. That written work is reasoning — you can prompt LLMs to do the same.

Understanding the topic

Core concepts:

• Chain-of-Thought (CoT): ask the model to think step by step.
• ReAct: alternates Thought / Action / Observation.
• Tree-of-Thought: explores multiple reasoning paths.
• Reasoning models (o1, o3) reason internally before answering.
• Reasoning costs more tokens but unlocks harder tasks.

Syntax reference

Visual workflow / architecture:

Thought  → "I should search the web"
Action   → search("OpenAI pricing")
Observation → "$0.01 / 1K tokens"
Thought  → "Now compute monthly cost"
Action   → calc(0.01 * 1_000_000)
Observation → "$10"
Final    → "$10/month

Real-world use

Math, code generation, planning, multi-hop QA, agent tool selection — all benefit from explicit reasoning. ChatGPT's 'think longer' mode is reasoning surfaced to the user.

Best practices

• Ask the model to 'think step by step' for hard tasks.
• Use reasoning models only when the answer's quality justifies the cost.

Common mistakes

• Forcing CoT on trivial tasks — slower + costlier with no gain.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What is Chain-of-Thought prompting?
• Q2. ReAct vs plain CoT — difference?
• Q3. When would you NOT enable reasoning?
• Q4. Why are reasoning models more expensive?