Chain-of-Thought Planning

One of the most important discoveries in modern AI is that large language models reason far better when they are explicitly told to think step by step.

Instead of jumping straight to an answer, the model first produces a sequence of intermediate reasoning steps. This technique is called Chain-of-Thought (CoT) prompting.

Classic example:

Question: A train travels 60 km in 1 hour. How far will it travel in 3 hours?

Chain-of-Thought:
• Speed = 60 km/h
• Time = 3 hours
• Distance = speed × time
• Distance = 60 × 3 = 180 km

Answer: 180 km

The explicit reasoning chain dramatically reduces errors on math, logic, and multi-step problems.

The Chain-of-Thought Discovery

Chain-of-Thought was introduced in the 2022 paper:

“Chain-of-Thought Prompting Elicits Reasoning in Large Language Models”
Wei et al.

The breakthrough insight was simple: adding the phrase “Let’s think step by step” (or similar) to a prompt often doubles or triples reasoning accuracy on complex tasks.

How Chain-of-Thought Works in Agents

In agent systems, CoT is typically used inside the reasoning stage of the loop. It turns raw LLM outputs into structured, traceable plans.

Example inside an agent (GPU comparison task):

Goal: Compare RTX 4090 and H100 for machine learning workloads

Thought 1: I need benchmark data on training throughput and memory bandwidth.
Thought 2: I should also gather pricing and power consumption numbers.
Thought 3: Finally compare suitability for training vs inference.

This linear sequence acts as a lightweight plan, giving the agent clear direction before it starts calling tools.

Implementing Chain-of-Thought

CoT prompting requires almost no code changes — just a better prompt.

def generate_with_cot(prompt: str, llm) -> str:
    full_prompt = f"""
    Solve the following task step by step.
    Show your reasoning clearly before giving the final answer.

    Task: {prompt}
    """
    return llm.generate(full_prompt)

fn generate_with_cot(task: &str, llm: &LLMClient) -> String {
    let prompt = format!(
        "Solve the following task step by step.\n\
         Show your reasoning clearly before the final answer.\n\n\
         Task: {}",
        task
    );
    llm.generate(&prompt)
}

Modern agents often combine CoT with structured output (JSON mode) or few-shot examples for even better consistency.

Why Chain-of-Thought Improves Performance

CoT works for three key reasons:

• Reduced cognitive load — The model solves one small step at a time instead of the entire problem at once.
• Explicit working memory — Each reasoning step stays visible in the context, preventing the model from “forgetting” earlier logic.
• Error detection — Mistakes become visible and correctable before the final answer.

When Chain-of-Thought Works Best

Limitations of Chain-of-Thought

CoT is not a silver bullet:

• Single-path reasoning — It follows one linear chain and cannot explore alternatives.
• Error propagation — An early mistake can derail the entire chain.
• Context bloat — Very long reasoning traces can fill the context window and increase cost.
• Exploration-heavy tasks — Creative problem solving or strategy games often need branching (Tree-of-Thought) instead.

Chain-of-Thought vs ReAct

In practice, the two are frequently combined: the agent first uses CoT to build a plan, then switches to ReAct to execute it.

Looking Ahead

Chain-of-Thought was the first major leap in LLM reasoning. It remains a foundational technique in 2026, used inside almost every advanced agent system.

However, its linear nature has natural limits. The next evolution addresses those limits by allowing the model to explore multiple reasoning paths.

→ Continue to 3.4 — Tree-of-Thought and Algorithm-of-Thought: Branching strategies that go beyond single linear chains.