Welcome to the third blog in WeCloudData’s Prompt Engineering Series! A famous software design principle by Robert C. Martin from his book Clean Code states: “Functions should do one thing. They should do it well. They should do it only.” This idea of emphasizing clarity and focus is equally important to prompt engineering – Prompts should be constructed carefully to achieve their desired goal without ambiguity or complexity. This blog is a brief introduction to the two major Prompt Engineering techniques: Zero-shot prompting and Few-shot Prompting.
If you’re new to Prompt Engineering, check out WeCloudData’s introductory post on Prompt Engineering here(add link) to make yourself familiar with the concept. Let’s get started!
What is Shot Prompting
AI systems learn best by examples. You can improve the performance of Generative AI systems by giving them instruction. This method is called In-Context Learning. In-Context Learning allows AI systems to learn from the example given in prompts rather than additional training. Shot-based prompting methods are closely related to in-context learning, where “shots” means the number of examples included in the prompt. AI models learn well and give generalized results with more shots (number of examples). Here is the breakdown of of shot-based prompting;
- Zero-Shot Prompting
- One-Shot Prompting
- Few-Shot Prompting

Zero-Shot Prompting
In zero shot prompting direct instruction is provided. It means AI systems have to depend on their pre-trained knowledge to figure out how to complete the target problem .It is the simplest form of prompting which can work well for the simple task, but fails to get the desirable outcomes for complex tasks.
Zero-Shot Prompting Examples
Here are some examples of Zero-shot prompting for Generative AI systems.
Prompt: Translation: ‘Translate ‘Hello’ to French’
Prompt: Text summarization: ‘Summarize this blog post (insert link)’
Prompt: Simple text classification

When to Use Zero-Shot Prompting
- For queries that don’t need contextual information.
- For simple tasks like to remove grammatical mistakes from text.
- When efficiency and simplicity are important.
- For text summarization and translation.
One-Shot Prompting
In one shot prompting only one example is given to clarify the task for the AI model. Although one-shot prompting provides the model with a starting point, it still struggles with complex tasks as more examples are needed to capture the range of potential results.
Example:
Here is examples of One-shot prompting for Generative AI systems.
Prompt: “If ‘Dog’ is known as ‘Chien’, what is ‘Cat’ known as?”
Few-Shot Prompting
Few-shot prompting involved providing an AI model with two or more examples , allowing it to understand the pattern and provide more precise answers. It is like having a mini introductory lesson before you need to do something new.
For example, you’ve never baked a particular type of cookie before. But instead of just diving in without proper guidance, you’re given a few quick recipes to check out first. The recipes create context learning. It helps you understand the basics of what you need to do for making cookies.
Apply the same scenario to the AI system. In few-shot prompts, an AI model which has been trained on huge data is given a small number of domain specific examples related to a new unseen task. These examples have the same effect as those recipes i.e., helping to guide AI on how to solve the target problem. Few-shot prompting is the same as having a mini crash course that helps you (or in this case, the AI) tackle something new more effectively.
Few-Shot Prompting Example:
Here are some examples of Few-shot prompting for Generative AI systems.
Prompt: “If ‘Cat’ is known as ‘Chat’, and ‘House’ is known as ‘Maison’, and ‘Dog’ is known as ‘Chien’, what is ‘River’ known as?”

When to Use Few-Shot Prompting:
- For content creation where specific tone, style, or structure is required.
- For complex sentiment analysis and code generation.
- In case of information extraction and conversational scenarios.
Experiment with both techniques to find the one that produces the best output result for your goal.
Differences Between Zero-Shot vs Few-Shot Prompting
To understand the difference between zero shot vs few shot: Zero-shot prompting involves giving an AI a simple instruction without any prior examples. To generate a response, the model relies only on the information it has learned during its training phase. While, few-shot prompting offers a more guided approach. By providing the AI with a few relevant examples alongside the instruction, you demonstrate how to approach the task. This helps the AI to produce more accurate and contextually appropriate outputs.
The primary trade-off between both of these prompt engineering methods are specificity and adaptability. Zero-shot prompting is quick and simple, making it perfect for situations where you need results quickly . While few-shot prompting frequently yields outputs that are far more consistent and customized – ideal for situations where accuracy is important.
Practical Applications: Test These Prompts Yourself
One-shot practice prompt
“Act as a content creator and write a 100-word post on how AI tools can improve time management and productivity.”
Few-shot Practice Prompt
“ I have several job descriptions for the Prompt Engineer position, and I would like to draft a more polished, comprehensive version of it. Please review the provided descriptions to extract the main responsibilities, the required skills and qualifications. Make sure the final job description is clear, concise, and engaging. Include a summary section as well, and arrange the information according to this format:
Job Title Key Responsibilities (bullet points) and Role Summary
Qualifications and Skills Needed (bullet points)
Required Skill (optional)
About the Company (optional; only included if it is included in the descriptions)
What’s Next?
This blog has covered two major techniques, differences between zero shot vs few shot, and effective prompts techniques for AI systems. Try the scenarios above and refine your prompts until you feel confident in your results. Remember, the key to great AI responses lies in mastering the art of prompting.
Next in WeCloudData’s Prompt Engineering Series, we’ll explore Chain-of-Thought Prompting. Stay tuned!