You already know how to give a large language model (LLM) instructions and a few examples. But when a task requires multiple logical steps, standard prompting often fails. The model might rush to a conclusion, misinterpret a step, or make a simple arithmetic error. It tries to answer in one go, which is where things break down.

Chain-of-Thought (CoT) prompting solves this by forcing the model to slow down and externalise its reasoning. Instead of just giving a final answer, it generates the intermediate steps it took to get there. This simple change dramatically improves performance on complex logic, maths, and commonsense reasoning tasks.

There are two main ways to trigger this reasoning process: Zero-Shot and Few-Shot CoT.

Zero-Shot CoT is the simplest approach. You append a simple phrase to your prompt, like "Let's think step-by-step." This acts as a trigger, encouraging the model to break down its process without any specific examples. It's a quick and easy way to boost performance on many tasks.

Few-Shot CoT is more deliberate and powerful. Instead of just a trigger phrase, you provide the model with high-quality examples, or exemplars, that demonstrate a complete reasoning process from question to answer. This is different from standard few-shot prompting, which only shows input-output pairs. Few-Shot CoT shows the model how to think.

Consider this comparison for a simple logic problem:

The CoT version doesn't just provide the answer; it explains the logical rule or real-world knowledge used to arrive at it. This gives the model a much stronger signal for how to approach the new problem.

Even with a well-crafted CoT prompt, an LLM can still make a mistake in a long reasoning chain. To build mission-critical systems, we need a way to catch these errors and increase our confidence in the final answer. This is where Self-Consistency comes in.

Instead of generating just one response, you ask the model to generate several different reasoning paths for the same problem. This is often achieved by increasing the 'temperature' parameter in your API call, which encourages more randomness in the output. Each path is a separate attempt at solving the problem.

Once you have multiple chains of thought, you don't just pick one. You look at the final answer from each chain and take a majority vote. The answer that appears most often is chosen as the definitive one. This simple technique is remarkably effective at filtering out errors. A single flawed calculation or logical leap in one path will be outvoted by other, correct paths.

By combining Few-Shot CoT with Self-Consistency, you create a robust system for tackling complex reasoning tasks. You guide the model with excellent examples and then use a voting mechanism to protect against the occasional hallucination or error. This dual approach is fundamental to building reliable, professional-grade applications with LLMs.

Time to check your understanding of these core reasoning techniques.

Mastering these techniques moves you from simply using LLMs to architecting intelligent systems that can reason reliably.