Chain-of-Thought & Few-Shot Prompting: A Plain English Guide for Marketers

Jargon vs. Reality

If you’ve spent any time on AI Twitter (or X), you’ve probably heard terms like “Few-Shot” and “Chain-of-Thought (CoT)”. They sound like advanced coding techniques, but they are actually just fancy words for common sense communication.

You don’t need a CS degree to use them. You just need to understand how to teach.

1. Few-Shot Prompting: The “Show, Don’t Just Tell” Method

Imagine hiring a new intern. If you say, “Write a tweet about our sale,” they might write something generic.

But if you say: “Write a tweet about our sale. Here are three examples of tweets we liked in the past:

• Tweet 1: ‘Blast off! 50% off starts NOW.’ (Short, emoji-heavy)
• Tweet 2: ‘Don’t miss out. The winter collection is here.’ (Direct, urgent)
• Tweet 3: ‘Ready to upgrade? Shop the look.’ (Question-based)”

That is Few-Shot Prompting. Research from Brown et al. (2020) on GPT-3 showed that providing 2 to 5 diverse examples significantly improves model performance and adherence to style.

How to Use It in Promptlyb

Instead of typing these examples every time, create a template:

Write a marketing email for {{product}}.
Adhere to the style of these successful past emails:
{{successful_email_examples}}

Now your team can paste in relevant examples for each campaign, ensuring the AI mimics your best work.

2. Chain-of-Thought: The “Show Your Work” Method

In a seminal paper by Wei et al. (2022), researchers at Google discovered that asking models to generate “intermediate reasoning steps” dramatically improved their ability to solve complex problems. This is Chain-of-Thought (CoT).

The Research: Why It Works

Wei’s team found that on complex math problems, standard prompting achieved only 18% accuracy, while Chain-of-Thought prompting boosted accuracy to 57% (and even higher with larger models).

Simply adding the phrase “Let’s think step by step” (known as Zero-Shot CoT, discovered by Kojima et al. (2022)) can trigger this reasoning capability.

Example

Standard Prompt: “Who is the ideal customer for this product?” (Model guesses).

CoT Prompt: “Think step-by-step:

1. Analyze the product features.
2. Identify the pain points these features solve.
3. Determine who experiences these pain points most acutely.
4. Based on this, define the ideal customer persona.”

By forcing the AI to “think aloud,” the final answer becomes significantly smarter and more grounded.

Putting It All Together

The best prompts often combine both:

1. Role: “You are a senior copywriter.”
2. Few-Shot: “Here are examples of our brand voice…”
3. Chain-of-Thought: “First, analyze the audience’s emotional state, then draft the headline.”

Conclusion

Don’t let the jargon scare you. “Few-Shot” just means Examples. “Chain-of-Thought” just means Step-by-Step. Master these two simple concepts, and you’re already ahead of 90% of AI users.

References

1. Wei, J., et al. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. arXiv:2201.11903
2. Kojima, T., et al. (2022). Large Language Models are Zero-Shot Reasoners. arXiv:2205.11916
3. Brown, T., et al. (2020). Language Models are Few-Shot Learners. arXiv:2005.14165